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  Tutorials

   Message from the Tutorials Chair    

Call For Tutorial And Workshop Proposals 2013   -  PDF file


Free tutorials planned

Two days of free tutorials and workshops (included with conference registration) presented by some of
the world’s foremost experts in topics of interest to genetic and evolutionary computation researchers and practitioners.



Introductory Tutorials

 
Genetic Algorithms
more info:
Erik Goodman   -  
Genetic Programming
more info:
Una-May ’Reilly   -  
Evolution Strategies: Basic Introduction
more info:
Thomas Bäck   -  
Evolutionary Computation: A unified view
more info:
Kenneth De Jong   -  
Evolutionary Multiobjective Optimization
more info:
Dimo Brockhoff    -  
Representations for evolutionary algorithms
more info:
Franz Rothlauf   -  
Evolutionary Neural Networks
more info:
Risto Miikkulainen   -  
Model-Based Evolutionary Algorithms
more info:
Dirk Thierens   -  
Peter A.N. Bosman   -  
Statistical Analysis for Evolutionary Computation Experiments: Introduction
more info:
Mark Wineberg   -  
Learning Classifier Systems: Introducing the user-friendly Textbook
more info:
Will N. Browne   -  
Ryan Urbanowicz   -  
Runtime Analysis of Evolutionary Algorithms: Basic Introduciton
more info:
Pietro S. Oliveto   -  
Per Kristian Lehre   -  

 


Advanced Tutorials

 
Evolution Strategies and CMA-ES (Covariance Matrix Adaptation)
more info:
Anne Auger   -  
Nikolaus Hansen   -  
Constraint-Handling Techniques used with Evolutionary Algorithms
more info:
Carlos Coello-Coello   -  
Elementary Landscapes: Theory and Applications
more info:
Darrell Whitley   -  
Andrew M. Sutton   -  
Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity
more info:
Frank Neumann   -  
Carsten Witt   -  
Fitness Landscapes and Graphs: Multimodularity Ruggedness and Neutrality
more info:
Sébastien Verel    -  
Black-Box Complexity: From Complexity Theory to Playing Mastermind
more info:
Benjamin Doerr   -  
Carola Doerr   -  
Advances on Evolutionary Many-objective Optimization
more info:
Hernan Aguirre   -  
Evolutionary Computation for Dynamic Optimization Problems (ECDOP)
more info:
Shengxiang Yang   -  
 


Specialized Techniques and Applications:

 
Expressive Genetic Programming
more info:
Lee Spector   -  
Cartesian Genetic Programming
more info:
Julian Miller   -  
Large Scale Data Mining using Genetics-Based Machine Learning
more info:
Jaume Bacardit    -  
Evolutionary Game Theory
more info:
Marco Tomassini    -  
Artificial Immune Systems for Optimization
more info:
Thomas Jansen   -  
Christina Zarges   -  
Generative and Developmental Systems
more info:
Kenneth Stanley   -  
Evolutionary Computation for Supervised Learning
more info:
Christian Gagné   -  
Differential Evolution: Recent Advances and Relative Performance
more info:
P N Suganthan   -  
Evolutionary Bilevel Optimization (EBO)
more info:
Pekka Malo  -  
Ankur Sinha    -  
Automatic (Offline) Configuration of Algorithms
more info:
Manuel López-Ibáñez   -  
Thomas Stützle   -  
Desiging and Building Powerful Inexpesive Robots for Evolutionary Research
more info:
Terence Soule   -  
Industrial Applications of Evolutionary Algorithms
more info:
Giovanni Squillero   -  
Computational Intelligence and Games
more info:
Daniele Loiacono    -   
Mike Preuss    -   
How to create meaningful and generalizable results
more info:
Thomas Bartz-Beielstein -
B. Naujoks   -  
M. Zaefferer    -  
Computational Aesthetic Evaluation: Automated Fitness Functions for Evolutionary Art, Design, and Music
more info:
Philip Galanter   -  
Open Issues in Genetic Programming
more info:
M. Oneill   -  
L. Vanneschi   -  
S. Gustafson   -  
W. Banzhaf   -  

 

 

Introductory Tutorials

 


Introduction to Genetic Algorithms

The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start “at the beginning,” describing first a “classical” genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance, described using the schema concept. It will cover some variations on the classical model, some successful applications of genetic algorithms, and advances that are making genetic algorithms more useful.

Erik Goodman
He is a Professor of Electrical & Computer Engineering, of Mechanical Engineering, and of Computer Science & Engineering at Michigan State University. He is Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center funded at $25 million in 2010. He studied genetic algorithms under John Holland at the University of Michigan, before they had been named. His use of a genetic algorithm in 1971 to solve for 40 coefficients of a highly nonlinear model of a bacterial cell was the first known GA application on a real-world problem, and required nearly a year for one run on a dedicated computer. He has developed and used evolutionary algorithms ever since, including for parameterization of complex ecosystem models, for evolution of cooperative behavior in artificial life, for factory layout and scheduling, for protein folding and docking, for design of composite structures, and for data mining and pattern classification. His recent research has centered on development of new types of parallel genetic algorithms and in design of mechatronic systems using genetic programming. He is co-founder and VP Technology of Red Cedar Technology, which provides tools for automated engineering design based on evolutionary computation. He chaired ICGA-97 and GECCO-2001, chaired GECCO's sponsoring organization, ISGEC, from 2001-2004, and was the founding chair of ACM SIGEVO, 2005-2007.



 


Genetic Programming

Genetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome.: genetic programming evolves a "program" to solve a problem rather than a single solution.

This tutorial introduces the basic genetic programming framework: It will explain how the powerful capability of genetic programming is derived from modular algorithmic components:

• executable representations - e.g. parse-tree, linear and graph-based variation operators that preserve syntax and explore a variable length, hierarchical solution space
• appropriately chosen programming functions
• fitness function specification

Una-MayO'Reilly
Dr. O'Reilly is a 10+ year veteran in the Evolutionary Algorithm community. Her doctoral thesis was the first comparative analysis of Genetic Programming (with respect to simulated annealing and hill climbing). She developed the first schema-based theoretical analysis of genetic programming. She now specializes in applications of evolutionary algorithms in tandem with machine learning and optimization. She leads the Evolutionary Design and Optimization group at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA. Her research currently focuses on evolutionary computation projects related to wind energy, sensory evaluation (symbolic regression) and computer systems (multi-core compilers and cloud virtualization technology)

Dr. O'Reilly developed a genetic programming based design system named Genr8 that is open-source and used throughout the world by researchers in Architecture. As VP of Technology at a Cambridge consulting firm, she led the development of a web-based name advisor based upon (patented) genetic algorithm, statistical learning and knowledge based technology. She has collaborated with State Street Global Advisors in their development of GP portfolio models that are used in investment strategies. Her current research sponsors include USA Dept of Energy (EXAScale project), DARPA (Angstrom project), VMWare, Givaudan Flavors PLC and Raytheon.

Dr. O'Reilly was a founding member of the International Society for Genetic and Evolutionary Computation executive board and is founding member and secretary of the ACM SIG-EVO Executive Board. She edited (with Peter Angeline) the first special issue on Genetic Programming in the journal Evolutionary Computation and is a founding editorial board member of the journal Genetic Programming and Evolvable Machines. She co-edited the second volume of Advances in Genetic Programming and the second and 5th volumes of Genetic Programming: From Theory to Practice. She has chaired GECCO (in 2005), the world's premier conference on Genetic and Evolutionary Computation. She serves on the editorial board of Evolutionary Computatino and as an action editor for Journal of Machine Learning Research.




 


Evolution Strategies: Basic Introduction


This tutorial introduces the basics of evolution strategies, a branch of evolutionary computation which focused already very early on the self-adaptation of strategy parameters such as mutation variances. Based on the original (1+1)-evolution strategy (using the 1/5 success rule for step size adaptation), the generalized (1,l) and (m,l)-strategies use the principle of self-adaptation mutation variances extensively. With both mutation and recombination, the (m,l)-strategy is a well known and understood algorithm for mixed-integer optimization (including the integer-only and the real-valued-only cases).

The tutorial will present all basic algorithmic variants as well as guidelines for their implementation. Moreover, extensions such as mixed-integer optimization and some derandomized variants of evolution strategies are also introduced briefly.

Concerning the comparison to genetic algorithms and evolutionary programming, we will use the concept of a generalized evolutionary algorithm as introduced in [1], and outline the similarities and differences of those classes of algorithms.

The tutorial will conclude with some examples of applications of evolution strategies.

References
[1] Th. Bäck (1996): Evolutionary Algorithms in Theory and Practice, Oxford University Press, NY.

Thomas Bäck

PhD, he is Professor for Natural Computing at the Leiden Institute for Advanced Computer Science (LIACS) at Leiden University, The Netherlands, and Chief Scientist of the Qe3 group at Netezza Corporation. Thomas received his PhD in Computer Science from Dortmund University, Germany, in 1994.

Thomas Bäck has more than 150 publications on natural computing technologies, as well as a book on evolutionary algorithms, entitled Evolutionary Algorithms: Theory and Practice. He is editorial board member and associate editor of a number of journals on evolutionary and natural computation, and has served as program chair for all major conferences in evolutionary computation.

His expertise lies in adaptive technologies for optimization and data-driven modeling, predictive analytics, and bioinformatics. He received the best dissertation award from the Gesellschaft für Informatik (GI) in 1995 and is an elected fellow of the International Society for Genetic and Evolutionary Computation for his contributions to the field.

 

 


Evolutionary Computation: A Unified View

The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to see the relationships between them, assess strengths and weaknesses, and make good choices for new application areas.

This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it.

Finally, the framework is used to identify some important open issues that need further research.

Kenneth A. De Jong

He received his Ph.D. in computer science from the University of Michigan in 1975. He joined George Mason University in 1984 and is currently a Professor of Computer Science, head of the Evolutionary Computation laboratory, and the Associate Director of the Krasnow Institute. His research interests include evolutionary computation, machine learning, and adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as heuristics for NP-hard problems, and the application of EAs to the problem of learning task programs in domains such as robotics, diagnostics, navigation and game playing. He is also interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly their control or the control of a benevolent teacher. He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of the ACM SIGEVO. He is the recipient of an IEEE Pioneer award in the field of Evolutionary Computation and a lifetime achievement award from the Evolutionary Programming Society.


 

 


Evolutionary Multiobjective Optimization

Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives.

Evolutionary Multiobjective Optimization (EMO) algorithms are widely used in practice for solving multiobjective optimization problems due to several reasons. As randomized blackbox algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run---opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovization) and it has been shown that certain single-objective problems become easier to solve with randomized search heuristics if the problem is reformulated as a multiobjective one (multiobjectivization).

This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, we are going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicator-based EMO, preference articulation, and performance assessment.

Though classified as introductory, this tutorial is intended for both novices and regular users of EMO. Those without any knowledge will learn about the foundations of multiobjective optimization and the basic working principles of state-of-the-art EMO algorithms. Open questions, presented throughout the tutorial, can serve for all participants as a starting point for future research and/or discussions during the conference.

 

Dimo Brockhoff

After obtaining his diploma in computer science (Dipl.-Inform.) from University of Dortmund, Germany in 2005, Dimo Brockhoff received his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. Between June 2009 and October 2011 he held postdoctoral research positions---first at INRIA Saclay Ile-de-France in Orsay and then at Ecole Polytechnique in Palaiseau, both in France. Since November 2011 he has been a junior researcher (CR2) at INRIA Lille - Nord Europe in Villeneuve d'Ascq, France . His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on many-objective optimization, benchmarking, and theoretical aspects of indicator-based search. 

 

 

 


Representations for Evolutionary Algorithms

Successful and efficient use of evolutionary algorithms (EAs) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation.

In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this representation.

Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance.

These concepts are
• locality and
• redundancy.

Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Furthermore, redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotypes. Finally, a bias need not be the result of the representation but can also be caused by the search operator.

The tutorial gives a brief overview about existing guidelines for representation design, illustrates the different aspects of representations, gives a brief overview of theoretical models describing the different aspects, and illustrates the relevance of the aspects with practical examples.

It is expected that the participants have a basic understanding of EA principles.

Franz Rothlauf
He received a Diploma in Electrical Engineering from the University of Erlangen, Germany, a Ph.D. in Information Systems from the University of Bayreuth, Germany, and a Habilitation from the University of Mannheim, Germany, in 1997, 2001, and 2007, respectively.

Since 2007, he is chair of Information Systems at the University of Mainz. He has published more than 90 technical papers in the context of planning and optimization, evolutionary computation, e-business, and software engineering, co-edited several conference proceedings and edited books, and is author of the books "Representations for Genetic and Evolutionary Algorithms" and "Design of Modern Heuristics".

His main research interests are the application of modern heuristics in planning and optimization systems. He is a member of the Editorial Board of Evolutionary Computation Journal (ECJ). Since 2007, he is member of the Executive Committee of ACM SIGEVO. He serves as treasurer for ACM SIGEVO since 2011. He has been organizer of many workshops and tracks on heuristic optimization issues, chair of EvoWorkshops in 2005 and 2006, co-organizer of the European workshop series on "Evolutionary Computation in Communications, Networks, and Connected Systems", co-organizer of the European workshop series on "Evolutionary Computation in Transportation and Logistics", and co-chair of the program commitee of the GA track at GECCO 2006. He was conference chair of GECCO 2009.

 

 

 

 


Evolutionary Neural Networks

Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.

Risto Miikkulainen
He is a Professor of Computer Sciences at the University of Texas at Austin. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His recent research focuses on methods for evolving neural networks and applying these methods to game playing, robotics, and intelligent control. He is an author of over 250 articles on neuroevolution, connectionist natural language processing, and the computational neuroscience of the visual cortex. He is an associate editor of IEEE Transactions on Computational Intelligence and AI in Games, IEEE Transactions on Autonomous Mental Development, and Cognitive Systems Research, and a member of the Board of Governors of the International Neural Networks Society.

 


Model-Based Evolutionary Algorithms

In model-building evolutionary algorithms the variation operators are guided by the use of a model that conveys as much problem-specific information as possible so as to best combine the currently available solutions and thereby construct new, improved, solutions. Such models can be constructed beforehand for a specific problem, or they can be learnt during the optimization process. Well-known types of algorithms of the latter type are Estimation-of-Distribution Algorithms (EDAs) where probabilistic models of promising solutions are built and subsequently sampled to generate new solutions.

Although EDAs are the best known example, other approaches also exist, such as the use of statistical models in the Linkage Tree Genetic Algorithm  (LTGA). In general, replacing traditional crossover and mutation operators by building and using models enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. This is an especially useful feature when considering optimization in a black-box setting. Successful applications include Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and scheduling.

This tutorial will provide an introduction and an overview of major research directions in this area.

Dirk Thierens
He is a lecturer at the Department of Information and Computing Sciences at Utrecht University, where he is teaching courses on Evolutionary Computation and Computational Intelligence. He received his PhD in Computer Science with a work on "Analysis and Design of Genetic Algorithms" (Leuven, 1995).  He has been involved in research on Evolutionary Computation for more than 20 years. Dirk Thierens is (has been) a member of the Editorial Board of the Evolutionary Computation journal, the Evolutionary Intelligence journal, the IEEE Transactions on Evolutionary Computation, and a member of the program committee of all major international conferences on evolutionary computation. He has co-chaired the GECCO workshop on Analysis and Design of Representations and Operators (2003),  and the GECCO workshop on Optimization by Building and Using Probabilistic Models (2004). He was co-chair of the program committee of the Genetic Algorithm track in GECCO-2004 and GECCO-2006, and Editor-in-Chief of GECCO-2007.

 

Peter A. N. Bosman
He is a senior scientist in the research group Multi-agent and Adaptive Computation at the Centrum Wiskunde & Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Peter was formerly affiliated with the Department of Information and Computing Sciences at Utrecht University, where also he obtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application estimation-of-distribution algorithms (EDAs). His current research position is mainly focused on fundamental EA research and on applications of EAs in energy systems, revenue management and the life sciences. Peter is best known for status of active researcher in the area of EDAs since its upcoming and has (co-)authored over 50 publications in the field of evolutionary computation.


 


Statistical Analysis for Evolutionary Computation Experiments: Introduction

Although the situation has been improving over the past few years, many researchers in the EC field are still using an unfortunate lack of statistical rigor when performing and analyzing experiments. Experimental results that lack a proper statistical analysis should be considered as anecdotal at best, and may even be wholly inaccurate. This tutorial aims at providing the basic statistical framework that all experimenters in the EC field should follow.

     The tutorial will cover introductory topics in Statistics such as the T test, confidence intervals, the Central Limit theorem (its power and limitations), non-parametric statistics, and will touch on multi-variate and multi-factorial analysis such as regression analysis, both linear and polynomial, as well as Analysis of Variance (ANOVA). The tutorial will be presented at an introductory level and is meant for any EC researchers who want to compare their newly designed EC system with established EC systems to see if there is an improvement, or who want to determine which EC system performs better on their chosen problem; i.e. nearly everyone.

     It is vital, if our community is to be taken seriously, for us to continue to educate ourselves (especially new Graduate students) on the statistical techniques that are considered de rigor for experiments performed in any field, such as ours, that is stochastic in nature.

 

Mark Wineberg
Prof. Wineberg has been actively researching the field of Evolutionary Computation (EC) and Genetic Algorithms (GA) since 1993 while he was still a graduate student at Carlton University, Ottawa, Canada. Over the years he has published on various topics including: the intersection of Genetic Algorithms and Genetic Programming, enhancing the GA for improved behavior in dynamic environments through specialized multiple populations, and exploring the concept of distances and diversity in GA populations. He is currently continuing his research in dynamic environments and studying the properties that allow one population to evolve more readily than another. Prof. Wineberg also teaches an undergraduate course at Guelph on computer simulation and modeling of discrete stochastic systems, which emphasizes proper statistical analysis, as well as a graduate course on experimental design and analysis for computer science, which is an outgrowth of the statistical analysis tutorial given at GECCO.


 

 


Learning Classifier Systems: Introducing the user-friendly Textbook

This tutorial will introduce the concepts of Learning Classifier Systems (LCS) based upon the new (and only) textbook on the field. This textbook is designed to be user-friendly in order to allow graduate students, EC researchers and industry/business practitioners who want to get up to speed with the field an easy path into using LCS to solve their complex problems. Learning Classifier Systems have been described as wondrous and inventing, but also as a swamp - this tutorial/book offers a boardwalk through the swamp. 'Wondrous' as Learning Classifier Systems combine the global search of Evolutionary Algorithms with the local optimisation of Reinforcement Learning to address classification and regression problems. The extracted knowledge though interacting with data or embedded in an environment is human readable. 'Inventing' as LCS' flexible nature allows application to many domains with many types of feedback on solution progress. But 'swampy' as an LCS is not a one line algorithm with separable methods and easily tuned parameters. 30+ years research on LCS has clarified understanding, produced algorithmic descriptions, determined 'sweet spots' for parameters and delivered understandable 'out of the box' code. This tutorial/textbook offers a user-friendly guide so that you will be able to proficiently implement LCS.  It will be through explanation based on the slides accompanying the book, examples supported by the Python code from the book and insight/narrative from the two authors. Tutorial participants will have online access to the textbook, slides and code to work through the examples themselves. Combined the presenters/authors have over 22 years' experience in the field of Learning Classifier Systems.  As well as publishing numerous Conference papers, Journal papers and source code, they have both chaired the International Workshop on Learning Classifier Systems. Dr Browne has been the co-track chair for the Genetics-Based Machine Learning (GBML) track, which includes Learning Classifier Systems at GECCO in 2011 and 2012.  The textbook arises from the experiences of lecturing a graduate course on LCS for two years and application of the technique in industrial / real-world problems.

Will Browne
He received a BEng Mechanical Engineering, Honours degree from the University of Bath, UK in 1993, MSc in Energy (1994) and EngD (Engineering Doctorate scheme, 1999) University of Wales, Cardiff. After eight years lecturing in the Department of Cybernetics, University Reading, UK, he was appointed Senior Lecturer, School of Engineering and Computer Science, Victoria University of Wellington, NZ in 2008. Dr. Browne's main area of research is Artificial Cognitive Systems, such as Learning Classifier Systems and Cognitive Robotics.

He has served as Co-track chair for Genetics-Based Machine Learning in Genetic and Evolutionary Computation Conference 2011 and 2012 [plus organizing committee of the International Workshop on Learning Classifier Systems (IWLCS) from 2009-2010]. Editor in chief for the Australasian Conference on Robotics and Automation 2012 and organized the Cognitive Robotics Intelligence and Control, EPSRC (UK)/NSF (USA) sponsored workshop 2006. Programme committee membership includes GECCO [2004-present], Congress on Evolutionary Computation [2004-present], Hybrid Intelligent Systems [2003-present], Parallel Problem Solving from Nature [2004-present] and Robot and Human Interactive Communication. Journal reviewer including: Journal of Soft Computing, IEEE Trans. on Evolutionary Computation, IEEE Trans. Systems Man and Cybernetics, Journal of Pattern Analysis and Applications, and Cognitive Computation.

Dr. Browne is a member of IMechE, ACM and the ACM SIGEVO group. He has published over 50 academic papers in books, refereed international journals and conferences

 

Ryan Urbanowicz
He holds a Ph.D in genetics from Dartmouth College, and both a M.Eng. and B.Eng. in agricultural and biological engineering from Cornell University.  His current research focuses on the development of machine learning strategies for feature selection, modeling, classification, and data mining in studies of common complex human disease.  In particular he is interested in developing strategies to deal with two phenomena which hinder these tasks, namely epistasis and genetic heterogeneity.  In 2009 he was awarded a Dartmouth Neukom Institute Fellowship funding the development of a learning classifier system (LCS) algorithm for the detection of complex multifactorial genetic associations predictive of disease.  To date Ryan has authored 8 international publications exploring the development and/or application of LCS algorithms including an extensive review of LCS algorithms, one which received best paper at GECCO 2010 in the Bioinformatics and Computational Biology track and another which received best paper at the Translational Bioinformatics Conference(TBC) in 2012.  He served on the IWLCS organizing committee from 2010-2012 and is returning as an organizer from 2012-2014.


 

 


Runtime Analysis of Evolutionary Algorithms: Basic Introduction

Evolutionary algorithm theory has studied the time complexity of evolutionary algorithms for more than 20 years. Different aspects of this rich and diverse research field were presented in at least three different advanced or specialized tutorials at last year's GECCO. This tutorial presents the foundations of this field. We introduce the most important notions and definitions used in the field and consider different evolutionary algorithms on a number of well-known and important example problems. Through a careful and thorough introduction of important analytical tools and methods, including fitness-based partitions, typical events and runs and drift analysis, by the end of the tutorial the attendees will be able to apply these techniques to derive relevant runtime results for non-trivial evolutionary algorithms. Moreover, the attendees will be fully prepared to follow the more advanced tutorials that cover more specialized aspects of the field. To assure the coverage of the topics required in the specialised tutorials, this introductory tutorial will be coordinated with the presenters of the more advanced ones. In addition to custom-tailored methods for the analysis of evolutionary algorithms we also introduce the relevant tools and notions from probability theory in an accessible form. This makes the tutorial appropriate for everyone with an interest in the theory of evolutionary algorithms without the need to have prior knowledge of probability theory and analysis of randomized algorithms.

Pietro S. Oliveto
He is an EPSRC funded Postdoctoral Fellow in Theoretical Computer Science at the University of Birmingham, UK. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. From October 2007 to April 2008, he was a visiting researcher at the Efficient Algorithms and Complexity Theory Institute at the Department of Computer Science of the University of Dortmund, Germany where he collaborated with Prof. Ingo Wegener's research group. From 2009 to 2010 he was a PhD+ Research Fellow at Birmingham funded by EPSRC. His main research interest is the time complexity analysis of randomised search heuristics. He has published several runtime analysis papers on Evolutionary Algorithms (EAs), Artificial Immune Systems (AIS) and Ant Colony Optimisation (ACO) algorithms for classical NP-Hard combinatorial optimisation problems, together with a review paper and a book chapter on the topic. His work has won best paper awards at GECCO 2008 and ICARIS 2011 and got very close at PPSN 2008, CEC 2009 and ECTA 2011 through best paper nominations. He is a member of the steering committee of the Theory of Randomised Search Heuristics (ThRaSH) workshop series, has co-organised ThRaSH 2009 and chaired the "Randomized Search Heuristics: Theoretical Aspects and Real World Applications" minisymposium at the 2011 Siam Conference on Optimization (SIAM-OPT 2011). He has presented a 4 hour introductory tutorial on the time complexity analysis of EAs at WCCI 2012 in Brisbane, Australia.

 

Per Kristian Lehre
He received MSc and PhD degrees in Computer Science from the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. He finished the PhD in 2006, and joined the School of Computer Science at The University of Birmingham, UK, as a Research Fellow in January 2007. He was a Post Doctoral Fellow at DTU Informatics, Technical University of Denmark in Lyngby, Denmark from April 2010. He is since September 2011 a Lecturer in the School of Computer Science at the University of Nottingham, UK. Dr Lehre's research interests are in theoretical aspects of nature-inspired search heuristics, in particular runtime analysis of population-based evolutionary algorithms.

 

 

 

 

Advanced Tutorials

 


Evolution Strategies and CMA-ES (Covariance Matrix Adaptation)

Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, R^n. Many of them can be formulated in a unified and comparatively simple framework.

This introductory tutorial focuses on the most relevant algorithmic question: how should the parameters of the sample distribution be chosen and, in particular, updated in the generation sequence? First, two common approaches for step-size control are reviewed, one-fifth success rule and path length control. Then, Covariance Matrix Adaptation (CMA) is discussed in depth: rank-one update, the evolution path, rank-mu update. Invariance properties and the interpretation as natural gradient descent are touched upon.

In the beginning, general difficulties in solving non-linear, non-convex optimization problems in continuous domain are revealed, for example non-separability, ill-conditioning and ruggedness. Algorithmic design aspects are related to these difficulties. In the end, the performance of the CMA-ES is related to other well-known evolutionary and non-evolutionary optimization algorithms, namely BFGS, DE, PSO,...

Anne Auger

Dr. Auger received her diploma in mathematics from the University of Paris VI, France, in 2001. She also obtained the French highest diploma for teaching mathematics, "Agregation de mathematiques". She received the doctoral degree from the university Paris VI in 2004. Afterwards, she worked for two years (2004-2006) as a postdoctoral researcher at ETH (in Zurich) in the Computational Laboratory (CoLab). Since October 2006, she holds a permanent research position at INRIA (French National Research Institute in Computer Science and Applied Mathematics). Her research interests are stochastic continuous optimization, including theoretical analyses of randomized search heuristics. She published more than fifteen articles at top conferences and journals in this area. She organized (and will organize) the biannual Dagstuhl seminar "Theory of Evolutionary Computation" in 2008 (and 2010).

 

Nikolaus Hansen

He is a senior researcher at The French National Institute for Research in Computer Science and Control (INRIA). Educated in medicine and mathematics, he received a Ph.D. in civil engineering in 1998 from the Technical University Berlin under Ingo Rechenberg. Before he joined INRIA, he has been working in applied artificial intelligence and genomics, and he has done research in evolutionary computation and computational science at the Technical University Berlin and the ETH Zurich. His main research interests are learning and adaptation in evolutionary computation and the development of algorithms applicable in practice. He has been the main driving force behind the development of CMA-ES over many years.

 


Constraint-Handling Techniques used with Evolutionary Algorithms

Evolutionary Algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be also discussed (as time allows).

Carlos Artemio Coello Coello
He received a BSc in Civil Engineering from the Universidad Autonoma de Chiapas in Mexico in 1991 (graduating Summa Cum Laude). Then, he was awarded a scholarship from the Mexican government to pursue graduate studies in Computer Science at Tulane University (in the USA). He received a MSc and a PhD in Computer Science in 1993 and 1996, respectively. Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor at DePauw University (in the USA). He is currently full professor at CINVESTAV-IPN in Mexico City, Mexico. He has published over 320 papers in international peer-reviewed journals, book chapters, and conferences. He has also co-authored the book "Evolutionary Algorithms for Solving Multi-Objective Problems", which is now in its Second Edition (Springer, 2007) and has co-edited the book "Applications of Multi-Objective Evolutionary Algorithms" (World Scientific, 2004). His publications currently report over 6,400 citations. He has delivered invited talks, keynote speeches and tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile, Colombia, Brazil, Argentina, China, India, Uruguay and Mexico. Dr. Coello has served as a technical reviewer for over 50 international journals and for more than 100 international conferences and actually serves as associate editor of the journals "Evolutionary Computation", "IEEE Transactions on Evolutionary Computation", "Computational Optimization and Applications", "Pattern Analysis and Applications" and the "Journal of Heuristics". He is also a member of the editorial boards of the journals "Soft Computing", "Engineering Optimization", the "Journal of Memetic Computing" and the "International Journal of Computational Intelligence Research". He received the 2007 National Research Award (granted by the Mexican Academy of Science) in the area of "exact sciences" and, since January 2011, he is an IEEE Fellow for "contributions to multi-objective optimization and constraint-handling techniques." He is also the recipient of the "2013 IEEE Kiyo Tomiyasu Award". He is member of the Mexican Academy of Science, and the ACM. His current research interests are: evolutionary multiobjective optimization and constraint-handling techniques for evolutionary algorithms.

 

 


Elementary Landscapes: Theory and Applications

The original theory of elementary landscapes focused on computing the mean of the local neighborhood search landscape for a few combinatorial optimization problems. The theory has now been generalized in several ways. First, it is now possible to compute statistical moments over neighborhoods. Second, the theory has been extended to provide statistical information over hyperspheres representing all neighbors at distance d on generalized hypercubes. Third, other combinatorial optimization problems such as MAX-kSAT and NK-Landscapes and QAP have been shown to be a superposition of a small number of elementary landscapes.

Taken together, these results make it possible to compute statistical moments up to a fixed order over exponentially large neighborhoods in polynomial time for many well-known combinatorial optimization problems. These methods are being used to develop new search strategies for MAX-SAT and NK-Landscapes and other problems. This includes extremely fast steepest ascent methods and search utilizing summary statistics that look multiple steps ahead.


Darrell Whitley

Dr. Whitley is Professor and Chair at Colorado State University. He is a former Chair of SIGEVO and former Editor-in-Chief of the journal Evolutionary Computation. He currently serves on the editorial board of Artificial Intelligence.

 

Andrew Sutton
He completed his Ph.D. research at Colorado State University in 2011. He currently holds a postdoctoral research position at the University of Adelaide, Australia studying theory of evolutionary algorithms, with a particular focus on parameterized runtime analysis on combinatorial optimization problems. In the past few years, Andrew has presented two tutorials at GECCO on elementary landscapes (with Darrell Whitley).

 

 


Bioinspired Computation in Combinatorial Optimization -- Algorithms and Their Computational Complexity

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.

The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.

The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com.

Frank Neumann

He studied Technical Computer Science in Dortmund and Kiel, Germany.
He received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively.
Currently, he is an associate professor  at the School of Computer Science, University of Adelaide, Australia. In his work, he considers evolutionary computation methods in particular for combinatorial and multi-objective optimization problems. With Ken De Jong he organised FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer.

 

Carsten Witt

He studied Computer Science at the Technical University of Dortmund, Germany, where he received his diploma and Ph.D. in 2000 and 2004, respectively. In spring 2009, he moved to the Technical University of Denmark, where he now works as an associate professor in algorithms. Carsten's main research interests are the theoretical aspects of randomized search heuristics, in particular evolutionary algorithms, ant colony optimization and particle swarm optimization. He co-organized the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and has given tutorials on the computational complexity of bio-inspired search heuristics in combinatorial optimization at GECCO 2008, PPSN 2008 (together with Frank Neumann), ThRaSH 2009 (together with Thomas Jansen) and at GECCO 2010. Together with Frank Neumann, he has authored the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity".

 


Fitness Landscapes and Graphs: multimodularity, ruggedness and neutrality

One of the most commonly-used metaphors to describe the process of heuristic search methods in solving combinatorial optimization problems is that of a fitness landscape. The landscape metaphor appears most commonly in work related to evolutionary algorithms, however, it can be used for search in general; the search space can be regarded as a spatial structure where each point (solution) has a height (objective function value) forming a landscape surface. In this scenario, the search process would be an adaptive-walk over a landscape that can range from having many peaks of high fitness flanked by cliffs falling to profound valleys of low fitness, to being smooth, with low hills and gentle valleys. Combinatorial landscapes can be seen as graphs whose vertices are the possible configurations. If two configurations can be transformed into each other by a suitable operator move, then we can trace an edge between them. The resulting graph, with an indication of the fitness at each vertex, is a representation of the given problem fitness landscape. The study of fitness landscapes consists in analyzing this graph or a relevant partition of it, with respect to the search dynamics or problem difficulty. This advanced tutorial will give an overview of the origins of the fitness landscape metaphor, and will cover alternative ways to define fitness landscapes in evolutionary computation. The two main geometries: multimodal and neutral landscapes, which correspond to two different graph partitions found in combinatorial optimization, will be considered, as well as the dynamics of metaheuristics searching on them. A short demonstration of using paradiseo software will be made to analyze the fitness landscape in practice. Furthermore, the relationship between problem hardness and fitness landscape metrics (i.e. autocorrelation, fitness distance correlation, neutral degree, etc), and the local optima network properties, studied in recent work, will be deeply analyzed. Finally, the tutorial will conclude with a brief survey of open questions and recent research directions on fitness landscapes such as multiobjective search space.

 

Sébastien Verel
He is an associate professor in Computer Science at the University of Nice Sophia-Antipolis, France, since 2006. He received a PhD in computer science from the University of Nice Sophia-Antipolis, France, in 2005. His PhD work was related to fitness landscape analysis in combinatorial optimization. He is a member of the Institut des Systèmes Complexes Paris Ile-de-France, and is an associated researcher at INRIA Lille - Nord Europe, France. His research interests are in the theory of evolutionary computation, cognitive science modeling, and complex systems.

 


Black-Box Complexity: From Complexity Theory to Playing Mastermind

As in classic algorithmics, runtime analysis of randomized search heuristics is most useful when it is complemented by a meaningful complexity theory.  In the last few years, black-box complexity---a notion suggested by Droste, Jansen, and Wegener in their seminal 2006 Theory of Computing Systems paper---has gained more and more attention. Many deep results have been developed, among others 2 GECCO best paper awards. The black-box complexity of a problem, in simple words, is the minimum number of fitness evaluations needed by any algorithm to solve it. Comparing the black-box complexity with the runtime of an evolutionary algorithm allows us to judge how good the algorithm is. If they are equal, no better evolutionary algorithm can exist.  For many problems, however, we observe that there is a gap between the black-box complexity and the runtime of the best known randomized search heuristic. For such problems, we need to analyze where this gap comes from. Ideally, this allows us to design better search heuristics. In this tutorial, we give a gentle introduction to black-box complexity, we survey the recent progress on this topic, and we discuss several directions for future research. Since one of the most basic black-box complexity problems turns out to be essentially the problem of playing the classic board game of Mastermind, we will talk about this and related guessing games as well.

Benjamin Doerr
He is a senior researcher at the Max Planck Institute for Computer Science and a professor at Saarland University. He received his diploma (1998), PhD (2000) and habilitation (2005) in mathematics from Kiel University. His research area is the theory both of problem-specific algorithms and of randomized search heuristics like evolutionary algorithms. Major contributions to the latter include runtime analyses for evolutionary algorithms and ant colony optimizers, as well as the further development of the drift analysis method, in particular, multiplicative and adaptive drift. In the young area of black-box complexity, he proved several of the current best bounds. Together with Frank Neumann and Ingo Wegener, Benjamin Doerr founded the theory track at GECCO and served as its co-chair 2007-2009. He is a member of the editorial boards of Evolutionary Computation and Information Processing Letters. Together with Anne Auger, he is an editor of the book "Theory of Randomized Search Heuristics".

Carola Doerr
She studied mathematics at Kiel University (Diploma in 2007) and computer science at the Max Planck Institute for Informatics and the Saarland University (PhD in 2011). Her PhD studies were supported by a Google Europe Fellowship in Randomized Algorithms. From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization. She is now a post-doc at the Université 7 in Paris and the Max Planck Institute for Informatics in Saarbrücken. Her research is supported by a Feodor Lynen Fellowship for postdoctoral researchers of the Humboldt foundation. Carola Doerr's main research interest is in the theory of randomized algorithms, both in the design of efficient algorithms as well as in randomized query complexities. She has published several papers about black-box complexities. She has contributed to the field of evolutionary computation also through results on the runtime analysis of evolutionary algorithms and drift analysis, as well as through the development of search heuristics for a geometric discrepancy problem.

 

 


Advances on Evolutionary Many-objective Optimization

Multi-objective evolutionary algorithms (MOEAs) are widely used in practice for solving multi-objective design and optimization problems. Historically, most applications of MOEAs have dealt with two and three objective problems, leading to the development of several evolutionary approaches that work successfully in these low dimensional objective spaces. Recently, there is a growing interest in industry to solve many-objective optimization problems, where the number of objective functions to optimize simultaneously is more than three. However, conventional MOEAs were not designed to cope with the challenges imposed by many-objective optimization and scale up poorly with the number of objectives of the problem. The development of robust, scalable, many-objective optimizers is an ongoing effort and a promising line of research. Critical to the development of such algorithms is an understanding of fundamental features of many-objective landscapes and the interaction between selection, variation, and population size to appropriately support the evolutionary search in high-dimensional spaces.

This tutorial aims at giving an introduction to evolutionary many-objective optimization, discussing important characteristics of many-objective landscapes and relating them to working principles, performance and behavior of the optimizers. Some of the recent research results will be presented in some detail. More specifically, the tutorial will (i) introduce the basic principles of multi-objective evolutionary algorithms, (ii) show scalability issues of conventional multi-objective optimizers when applied to many-objective problems, (iii) introduce important features of many-objective landscapes, (iv) show the effectiveness of selection and variation operators when the characteristics of the many-objective landscapes are taken into account, (v) show the effects of population size, (vi) present a general overview of the approaches to many-objective optimization, together with their state-of-the-art algorithms and techniques, (vii) present open question throughout the tutorial, that can serve for all participants as a starting point for future research and/or discussions during the conference.

Hernán Aguirre
He received his Engineer degree in computer systems from Escuela Politécnica Nacional, Ecuador, in 1992, and the M.S. and Ph.D. degrees from Shinshu University, Japan, in 2000 and 2003, respectively. Currently, he is an associate professor at Shinshu University. His research interests include evolutionary computation, multidisciplinary design optimization, and parallel computing. He has written over 100 international journal and conference research papers on evolutionary algorithms, focusing on the working principles of single-, multi-, and many-objective (any-objective) evolutionary optimizers, landscape analysis,and epistasis. He collaborates actively with industry and with the Japan Aerospace Exploration Agency (JAXA) on the development and application of many-objective evolutionary algorithms.

 


Evolutionary Computation for Dynamic Optimization Problems (ECDOP)

Many real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization problems (DOPs) are challenging problems for researchers and practitioners in decision-making due to their nature of difficulty. Yet, they are important problems that decision-makers in many domains need to face and solve. Evolutionary computation (EC) is a class of stochastic optimization methods that mimic principles from natural evolution to solve optimization and search problems. EC methods are good tools to address DOPs due to their inspiration from natural and biological evolution, which has always been subject to changing environments. EC for DOPs has attracted a lot of research effort during the last twenty years with some promising results. However, this research area is still quite young and far away from well-understood. This tutorial aims to summarise the research area of EC for DOPs and attract potential young researchers into the important research area. It will provide an introduction to the research area of EC for DOPs and carry out an indepth description of the state-of-the-art of research in the field regarding the following five aspects: benchmark problems and generators, performance measures, algorithmic approaches, theoretical studies, and applications. Some future research issues and directions regarding EC for DOPs will also be presented. The purpose is to (i) provide clear definition and classification of DOPs; (ii) review current approaches and provide detailed explanations on how they work; (iii) review the strengths and weaknesses of each approach; (iv) discuss the current assumptions and coverage of existing research on EC for DOPs; and (v) identify current gaps, challenges, and opportunities in EC for DOPs.

Shengxiang Yang
(http://www.tech.dmu.ac.uk/~syang/) He is now a Professor of Computational Intelligence (CI) at the Centre for Computational Intelligence (http://www.cci.dmu.ac.uk/), De Montfort University (DMU), UK. Before joining DMU in July 2012, he has worked in the UK at Brunel University, University of Leicester, and King's College London, from October 1999 to June 2012, respectively. He has worked extensively for over 15 years in the areas of CI methods, including EC and artificial neural networks, and their applications for realworld problems. He has over 150 publications in these domains, including over 40 SCI journal papers. His work has been supported by UK research councils (e.g., EPSRC, Royal Society, and Royal Academy of Engineering), Chinese Ministry of Education, Hong Kong Polytechnic University Research Grants, and industry partners (e.g., BT, Honda, Railway Safety and Standards Board, and Network Rail, etc.), with a total funding of approximately £1M to him as the Principle Investigator (PI), of which two EPSRC standard research projects have been focused on EC for DOPs. He is an Associate Editor or Editorial Board Member of 4 international journals, including the MIT Press journal – Evolutionary Computation. He is the founding chair of the Task Force on Intelligent Network Systems (TF-INS) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs) of the IEEE CI Society (CIS). He has chaired over 10 workshops and special sessions relevant to ECiDUEs for several major international conferences. He is the founding co-chair of the IEEE Symposium on CI in Dynamic and Uncertain Environments. He has been on the program committee for over 40 conferences. He has co-edited 8 books, proceedings, and journal special issues. He has given a number of invited keynote speeches at international conferences and workshops, and seminars in different countries. Since 2003, Prof. Yang has played a world leading role in the rapidly growing area of EC for DOPs. He has developed several DOP benchmark generators, has devised several advanced schemes, e.g., associative memory, dualism, and guided immigrants, for EC for DOPs, and has developed several state-of-the-art EC methods for DOPs. Both the benchmark generators and state-of-the-art EC methods developed for DOPs are being widely used by other researchers to test and compare their optimization algorithms for DOPs.

 

 

Specialized Techniques and Applications:

 


Expressive Genetic Programming

The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as elucidated by formal language theory, and from the ease with which various computational structures can be produced by random code generation and by the action of genetic operators. Highly expressive languages can facilitate the evolution of programs for any computable function using, when appropriate, multiple data types, evolved subroutines, evolved control structures, evolved data structures, and evolved modular program and data architectures. In some cases expressive languages can even support the evolution of programs that express methods for their own reproduction and variation (and hence for the evolution of their offspring).

This tutorial will begin with a comparative survey of approaches to the evolution of programs in expressive programming languages ranging from machine code to graphical and grammatical representations. Within this context it will then provide a detailed introduction to the Push programming language, which was designed specifically for expressiveness and specifically for use in genetic programming systems. Push programs are syntactically unconstrained but can nonetheless make use of multiple data types and express arbitrary control structures, supporting the evolution of complex, modular programs in a particularly simple and flexible way. The Push language will be described and demonstrated, and ten years of Push-based research, including the production of human-competitive results, will be briefly surveyed. The tutorial will conclude with a discussion of recent enhancements to Push that are intended to support the evolution of complex and robust software systems.

Lee Spector
He is a Professor of Computer Science in the School of Cognitive Science at Hampshire College in Amherst, Massachusetts, and an adjunct professor in the Department of Computer Science at the University of Massachusetts, Amherst. He received a B.A. in Philosophy from Oberlin College in 1984 and a Ph.D. from the Department of Computer Science at the University of Maryland in 1992. His areas of teaching and research include genetic and evolutionary computation, quantum computation, and a variety of intersections between computer science, cognitive science, evolutionary biology, and the arts. He is the Editor-in-Chief of the journal Genetic Programming and Evolvable Machines (published by Springer) and a member of the editorial board of Evolutionary Computation (published by MIT Press). He is also a member of the SIGEVO executive committee and he was named a Fellow of the International Society for Genetic and Evolutionary Computation.

More info: http://hampshire.edu/lspector

 

 

 


Cartesian Genetic Programming

Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with Peter Thomson in 1997. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). This tutorial is will cover the basic technique, advanced developments and applications to a variety of problem domains. It is given by two of the leading researchers in CGP. The first edited book on CGP was published by Springer in September 2011. CGP has its own dedicated website http://www.cartesiangp.co.uk

Julian Miller
He is an academic in the Department of Electronics at the
University of York. His main research interests are genetic
programming (GP), and computational development. He
has published over 200 refereed papers on evolutionary
computation, genetic programming, evolvable hardware,
and computational development and other topics. He has
been chair or co-chair of sixteen conferences or workshops
in genetic programming, computational development,
evolvable hardware and evolutionary techniques.

Dr. Miller chaired of the Evolvable Hardware tracks at the
Genetic and Evolutionary Computation Conference in
2002-2003 and was Genetic Programming track chair in
2008. He was co-chair the Generative and Developmental
Systems(GDS) track in 2007, 2009 and 2010. He is an
associate editor of the Genetic Programming and Evolvable
Machines and a former associate editor of IEEE
Transactions on Evolutionary Computation. He is an
editorial board member of the journals Evolutionary
Computation and Unconventional Computing. He received
the Evostar award in 2011 for outstanding contribution in
evolutionary computation.

 

 


Large Scale Data Mining using Genetics-Based Machine Learning

We are living in the peta-byte era. We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task. Recent advances in representations, learning paradigms, and theoretical modeling have show the competitiveness of non EC techniques in herding large scale data analysis. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented computational resources on the edge of petascale computing. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.

This tutorial will try to shed light to the above mentioned questions, following a roadmap that starts exploring what large scale means, and why large is a challenge and opportunity for GBML methods. As we will show later, opportunity has multiple facets: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms, and alternative programming models, each of them helping to make GBML very attractive for large-scale data mining. Given these building blocks, we will continue to unfold how can we model the scalability of the components of GBML systems targeting a better engineering effort that will make embracing large datasets routine. Finally, we will illustrate how all these ideas fit by reviewing real applications of GBML systems and what furtherdirections will require serious consideration.

Jaume Bacardit
He received a BEng and MEng in Computer Engineering and a PhD in Computer Science from the Ramon Llull University in Barcelona, Spain in 1998,2000 and 2004, respectively. His PhD thesis involved the adaptation and application of Learning Classifier Systems to Data Mining tasks in terms of scalability, knowledge representations and generalization capacity. In 2008 he was appointed as a Lecturer in Bioinformatics at the University of Nottingham. His post was created to champion interdisciplinary work between computer science and the biological sciences. His research interests include the application of evolutionary Learning to data mine large-scale challenging datasets and, in a general sense, the use of data mining and knowledge discovery for biological domains. He has more than 40 refereed international publications between journal papers, conference papers and book chapters, has given 8 invited talks and co-edited two books. From 2007 to 2010 he was the co-organizer of the International Workshop on Learning Classifier Systems and in 2009 and 2013 he is/was the (co)chair the Genetics-Based Machine Learning track of GECCO. His work won the best-paper award of the Genetics-Based Machine Learning track of GECCO in 2010 and 2011.

 

 


Evolutionary Game Theory

Evolutionary game theory has been introduced essentially by biologists in the seventies and has immediately diffused into economical and sociological circles. Today, it is a main pillar of the whole edifice of game theory and widely used both in theory and in applications. This tutorial aims at presenting evolutionary game theory in an easy, yet rigorous way, and to relate it with other approaches in game theory. The material presented does not require a previous acquaintance with standard game theory: these fundamentals will be developed in the first part of the tutorial, which is self-contained. In the second part the main concepts of the evolutionary and dynamical approach will be introduced, namely the concept of an evolutionarily stable strategy and the replicator dynamics. The analogies between Nash equilibria, evolutionarily stable strategies, and rest points of the dynamics will be explained. All the concepts will be illustrated using simple well known paradigmatic games such as the Prisoner's Dilemma, Hawks and Doves, and coordination games among others. Finally, some recent exciting trends in evolutionary games on networks will be introduced and discussed.

Marco Tomassini
He is a professor of Computer Science at the Information Systems Department of the University of Lausanne, Switzerland. He got a Doctor's degree in theoretical chemistry from the University of Perugia, Italy, working on computer simulations of condensed matter systems. His current research interests are  centered around the application of biological ideas to artificial systems.

He is active in evolutionary computation, especially spatially structured systems, genetic programming, and the structure of program search spaces.

He is also interested in machine learning, evolutionary games,
and the dynamical properties of networked complex systems. He has been Program Chairman of several international events and has published many scientific papers and several authored and edited books in these fields. He has received the EvoStar 2010 Award in recognition for oustanding contribution to evolutionary computation.

 

 


Artificial Immune Systems for Optimization

Artificial immune systems (AIS) are a class of biologically inspired algorithms which are build after different theories from immunology. While the field of AIS is a relatively new area of research, it has achieved numerous promising results in different areas of application, e.g., learning, classification, anomaly detection, and optimisation. In this tutorial we focus in particular on AIS build for the purpose of optimisation. From an algorithmic point of view AIS show on a high level similarities to other biologically inspired algorithms, e.g. evolutionary algorithms. Due to their different origin concrete AIS for optimisation are quite different from evolutionary algorithms. They constitute an interesting alternative approach to current methods.

The tutorial gives an overview over different methods in the field of AIS. It addresses everyone who wants to broaden his or her area of research within this emerging field, both practitioners and theoreticians. It enables attendees without prior knowledge of AIS to learn about a novel kind of optimisation method that can be used as an alternative to other biologically inspired algorithms. Moreover, it gives researchers with prior knowledge of AIS the opportunity to deepen their understanding of the considered algorithms.

We start with an overview over the different areas of AIS, including different general approaches and some immunological background. Afterwards, we discuss several examples of AIS for optimisation. We introduce concrete algorithms and their implementations and point out similarities and differences to other biologically inspired algorithms. In the last part of the tutorial, we present an overview over recent theoretical results for this kind of algorithms.

Thomas Jansen
He is Senior Lecturer at the Department of Computer Science at Aberystwyth University, Wales, UK (since January 2013). He studied Computer Science at the University of Dortmund, Germany, and received his diploma (1996, summa cum laude) and Ph.D. (2000, summa cum laude) there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De Jong's EClab at the George Mason University in Fairfax, VA. He was Junior professor for Computational Intelligence from September 2002 to February 2009 at the Technical University Dortmund. From March 2009 to December 2012 he was Stokes Lecturer at the Department of Computer Science at the University College Cork, Ireland. He has published 18 journal papers, 39 conference papers, contributed seven book chapters and authored one book on evolutionary algorithm theory. His research is centred around design and theoretical analysis of artificial immune systems, evolutionary algorithms and other randomised search heuristics. He is associate editor of Evolutionary Computation (MIT Press), member of the steering committee of the Theory of Randomised Search Heuristics workshop series, is co-track chair of the Genetic Algorithm track of GECCO 2013, was program chair at PPSN 2008, co-organised FOGA 2009, organised a workshop on Bridging Theory and Practice (PPSN 2008), two GECCO workshops on Evolutionary Computation Techniques for Constraint Handling (2010 and 2011) and Dagstuhl workshops on Theory of Evolutionary Computation (2004 and 2006) and on Artificial Immune Systems (2011).

 

 

Christine Zarges
She is a Birmingham Fellow in the School of Computer Science at the University of Birmingham, UK. Before that she spent 12 months as a visiting PostDoc at the University of Warwick, UK, supported by a scholarship of the German Academic Research Service (DAAD). She studied Computer Science at the TU Dortmund, Germany, and obtained her Diploma (2007) and PhD (2011, Theoretical Foundations of Artificial Immune Systems) there. Her dissertation was awarded the dissertation award of the TU Dortmund. In 2010 she received a Google Anita Borg Memorial Scholarship. Her research focuses on the theoretical analysis of randomised algorithms and processes, in particular randomised search heuristics such as artificial immune systems and evolutionary algorithms. She is also interested in computational and theoretical aspects of immunology. Two of her papers on artificial immune systems were awarded a best paper award at leading conferences (PPSN 2008 and ICARIS 2011). She is member of the editorial board of Evolutionary Computation (MIT Press).

 


Generative and Developmental Systems

 In evolutionary computation it is common for the genotype to map directly to the phenotype such that each gene in the genotype represents a single discrete component of the phenotype. While this convention is widespread, in nature the mapping is not direct. Instead, the genotype maps to the phenotype through a process of development, which means that each gene may be activated multiple times and in multiple places in the process of constructing the phenotype.

This tutorial will examine why such indirect mapping may be critical to the continuing success of evolutionary computation. Rather than just an artifact of nature, indirect mapping means that vast phenotypic spaces (e.g. the 100 trillion connection human brain) can be searched effectively in a space of far fewer genes (e.g. the 30,000 gene human genome). The tutorial will introduce this research area, called Generative and Developmental Systems (GDS), by surveying milestones in the field and introducing its most profound puzzles. Most importantly, what is the right abstraction of natural development to capture its essential advantages without introducing unnecessary overhead into the search?

Kenneth O. Stanley
He is an associate professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems (GDS), coevolution, machine learning for video games, and interactive evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. He is an associate editor of IEEE Transactions on Computational Intelligence and AI in Games, on the editorial board of Evolutionary Computation journal, on the ACM SIGEVO Executive Committee, and a co-founder and former track co-chair of the GDS track at GECCO.  He is also a co-founder and the editor-in-chief of aigameresearch.org.

 

 


Evolutionary Computation for Supervised Learning

Supervised learning is a machine learning task that aims at inferring a function from labelled training data. The training data consists of pairs of input values, often represented as a fixed-size real-valued vector, and desired output value. The type of output value determine the type of tasks to achieve, foremostly   classification, where the output value is a discrete class and the inferred function is referred as a classifier, and regression, where the output is a real number and the inference process lead to a regression function. The objective of supervised learning is to infer a function that generalizes well, that is a function that will be able to produce an output value from an unseen input data with a relatively low error value according to a given performance measure. The design of a supervised learning method includes defining three elements: 1) the representation, which involves defining the features that will capture the key information on the input data used for the inference; 2) the evaluation method, which allows to determine whether a classifier or regression function performs well on the training set in order to generalize well on unseen data; and 3) the optimization method, which allows to search the space of hypothesis efficiently, in order to propose a classifier or regression function that performs well according to the evaluation method. Evolutionary Computation (EC) provides a rich set of methods that can be beneficial for supervised learning, by making an optimization of some aspects of a supervised learning system, either working on non-regular representations (e.g. variable-length) or handling some non-conventional evaluation methods that are hardly usable by other methods (e.g. strongly non-convex optimization, multi-objective optimization).

This tutorial will start with an introduction to supervised learning and its main methods. The remaining of the presentation will consist in an editorial choice of a various set of relevant methods, with critical discussions on their strong aspects and their limitations. The preliminary programme of the presentation includes methods for features selection based on evolutionary multiobjective optimization, features construction with Genetic Programming (GP), distance/similarity function construction with GP, symbolic regression with GP, generation of classification trees by GP, dynamic subset selection, hold-out and cross-validation methods in EC, members selection method for making ensembles, diversity production with EC for ensemble methods, and optimization with non-conventional evaluation methods (e.g. AUC-ROC).

Christian Gagné
He is assistant professor of computer engineering at Université Laval in Québec (Canada) since 2008, and member of the Computer Vision and Systems Laboratory. He received is B.Ing. (computer engineering), M.Sc. (electrical engineering) and Ph.D. (electrical engineering) from Université Laval in 2000, 2003 and 2005, respectively. In the 2005-2006 period, he was ERCIM postdoctoral fellow jointly at the INRIA Saclay-Ile-de-France in Orsay (France) and the University of Lausanne (Switzerland). He also worked as research analyst for Informatique WGZ (2006-2007) and MacDonald, Dettwiler and Associates (2007), as consultant on research projects with Defence R&D Canada -- Valcartier. His research interests are on the engineering of distributed intelligent systems, in particular systems involving evolutionary computation (genetic programming, co-evolution, genetic-based machine learning), machine learning (reinforcement learning, ensemble methods, pattern recognition), and distributed computing (sensor networks, autonomic computing, high-performance computing). Prof. Gagné was local chair of GECCO 2009, competition chair at GECCO 2010, and is chair of the Digital Entertainment Technology and Art track at GECCO 2011. He is also the main author of Open BEAGLE, a C++ evolutionary computation framework.

 

 

 


Differential Evolution: Recent Advances and Relative Performance

Differential Evolution (DE) is arguably one of the most powerful stochastic realparameter optimization algorithms of current interest. DE operates through similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This tutorial will begin with a detailed overview of the basic concepts related to DE, its algorithmic components and control parameters. It will subsequently discuss some of the significant algorithmic variants of DE for bound constrained single-objective optimization. Application of the DE family of algorithms for multi-objective, constrained, large-scale, dynamic and multi-modal optimization problems will also be uncovered. The talk will also discuss the effect of incorporating ensemble learning in DE – a novel concept applied to adapt the DE family for various kinds of optimization problems. Theoretical advances made to understand the search mechanism of DE and the effect of its most important control parameters will be discussed. The talk will finally discuss a few engineering applications of DE and uncover a few problems that pose severe challenge to the state-of-the-art DE algorithms and demand strong research effort from the DE-community in the future. The talk will also touch on other population based real-parameter optimization algorithms, characteristic distinctions of these algorithms with respect to DE and performance comparison among these real-parameter optimization algorithms.

Ponnuthurai Nagaratnam Suganthan
He received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He was a predoctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 1996–99. Since 1999 he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore where he was an Assistant Professor and now is an Associate Professor. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the IEEE Trans on Evolutionary Computation, Information Sciences (Elsevier), Pattern Recognition (Elsevier) and Int. J. of Swarm Intelligence Research Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier Journal. His co-authored SaDE (April 2009) paper won "IEEE Trans. on Evolutionary Computation" outstanding paper award. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, bioinformatics, applications of evolutionary computation and neural networks. His publications have been well cited (Googlescholar Citations). He is a Senior Member of the IEEE. Homepage: http://www3.ntu.edu.sg/home/epnsugan/

 

 


Evolutionary Bilevel Optimization (EBO)

Many practical optimization problems should better be posed as bilevel optimization problems in which there are two levels of optimization tasks. A solution at the upper level is feasible if the corresponding lower level variable vector is optimal for the lower level optimization problem. Consider, for example, an inverted pendulum problem for which the motion of the platform relates to the upper level optimization problem of performing the balancing task in a time-optimal manner. For a given motion of the platform, whether the pendulum can be balanced at all becomes a lower level optimization problem of maximizing stability margin. Such nested optimization problems are commonly found in transportation, engineering design, game playing and business models. They are also known as Stackelberg games in the operations research and computer science community. These problems are too complex to be solved using a classical optimization method simply due to the "nestedness" of one optimization task into another. Evolutionary optimization methodologies provide some amenable ways to solve such problems due to their flexibility and ability to handle constrained search spaces efficiently. Clearly, EC, as a field, has an edge in solving such difficult yet practically important problems. In the recent past, there has been a surge in research activities towards solving bilevel optimization problems. In this tutorial, we shall introduce principles of bilevel optimization for single and multiple objectives and discuss the difficulties in solving such problems in general. With a brief survey of the existing literature, we shall present a few viable evolutionary algorithms for both single and multi-objective EAs for bilevel optimization. Our recent studies on bilevel test problems and some application studies will be discussed. Finally, a number of immediate and future research ideas on evolutionary bilevel optimization (EBO) will be highlighted.

Pekka Malo
He is an Assistant Professor of Statistics in the Department of Information and Service Economy at Aalto University, Finland. He holds a PhD degree in quantitative methods from Helsinki School of Economics and M.Sc. degree in mathematics from Helsinki University. Before joining the department he worked as a business simulation developer at Cesim and head of research at Fosta Consulting. His research interests include evolutionary computation, machine learning, semantic information retrieval, artificial intelligence, and their applications to economics and finance.

 

 

Ankur Sinha
He is currently a post-doc researcher at the Aalto University School of Business. He has completed his PhD from Aalto University School of Business, Helsinki, Finland and Bachelor of Technology from the Indian Institute of Technology, Kanpur, India. His research interests are in the area of Bilevel Optimization, Multi-objective Optimization, Decision Making and Evolutionary Algorithms.

 


Automatic (Offline) Configuration of Algorithms

Most optimization algorithms, including evolutionary algorithms and metaheuristics, and general-purpose solvers for integer or constraint programming, have many parameters that need to be properly configured for obtaining the best performance on a particular problem. (Offline) Automatic algorithm configuration methods help algorithm users to determine the parameter settings that optimize the performance of the algorithm before the algorithm is actually deployed. Moreover, automatic algorithm configuration methods have the potential to lead to a paradigm shift in algorithm design and configuration because they enable algorithm designers to explore much larger design spaces than by traditional trial-and-error and experimental design procedures. Thus, algorithm designers can focus on inventing new algorithmic components, combine them in flexible algorithm frameworks, and let final algorithm design decisions be taken by automatic algorithm configuration techniques for specific application contexts.

This tutorial will be divided in two parts. The first part will give an overview of the algorithm configuration problem, review recent methods for automatic algorithm configuration, and illustrate the potential of these techniques using recent, notable applications from the presenters' and other researchers work. The second part of the tutorial will focus on a detailed discussion of more complex scenarios, including multi-objective problems, anytime algorithms, heterogeneous problem instances, and the automatic generation of algorithms from algorithm frameworks. The focus is, hence, on practical but challenging applications of automatic algorithm configuration. In this second part of the tutorial we demonstrate how to tackle these configuration tasks using our irace software (http://iridia.ulb.ac.be/irace), which implements an iterated racing procedure for automatic algorithm configuration. We will provide a practical step-by-step guide on using irace for the typical algorithm configuration scenario.

Manuel López-Ibáñez
He is a postdoctoral researcher (Chargé de recherche) of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles, Belgium. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published journal papers on diverse areas such as evolutionary algorithms, ant colony optimization, multi-objective optimization, pump scheduling and various combinatorial optimization problems. His current research interests are algorithm engineering, experimental analysis and automatic configuration of stochastic optimization algorithms, for single and multi-objective optimization. He is the lead developer and current maintainer of the irace software package (http://iridia.ulb.ac.be/irace).

 

Thomas Stützle
Dr Stützle. is a research associate of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles (ULB), Belgium. He received the Diplom (German equivalent of M.S. degree) in business engineering from the Universität Karlsruhe (TH), Karlsruhe, Germany in 1994, and his PhD and his habilitation in computer science both from the Computer Science Department of Technische Universität Darmstadt, Germany. He is the co-author of two books about "Stochastic Local Search: Foundations and Applications" and "Ant Colony Optimization" and he has extensively published in the wider area of metaheuristics including 18 edited proceedings or books, 7 journal special issues, and more than 170 journal, conference articles and book chapters, many of which are highly cited. He is associate editor of Computational Intelligence and Mathware & Soft Computing and on the editorial board of five other journals including Evolutionary Computation and Swarm Intelligence. His main research interests are in metaheuristics, swarm intelligence, methodologies for engineering stochastic local search algorithms, multi-objective optimization, and automatic algorithm configuration. In fact, since more than a decade he is interested in automatic algorithm configuration and design methodologies and he has contributed to some effective algorithm configuration techniques such as F-race, Iterated F-race and ParamILS. His 2002 GECCO paper on "A Racing Algorithm For Configuring Metaheuristics" (joint work with M. Birattari, L. Paquete, and K. Varrentrapp) has received the 2012 SIGEVO impact award.

 


Desiging and Building Powerful Inexpesinve Robots for Evolutionary Research

This tutorial will cover how to design, build, and program computationally powerful, inexpensive Robots for evolutionary computation applications using Commodity Off The Shelf Robots (COTSBots) parts.  The tutorial will emphasize robots for on-line and on-board evolutionary learning, cooperative learning, and cooperative behaviors and include hands-on demonstrations.  Our basic solderless COTSBot design consists of an Android smartphone or net book computer, an Arduino microcontroller, and an RC car or similar mobility platform.  The result is a low cost (<$600), feature-rich, robot with the computational power to support research in evolutionary robotics including on-board and on-line evolution.  The robots use modern development environments and have built-in, software accessible web cameras, wireless and Bluetooth capabilities, accelerometers, GPS, microphones, speakers, etc.  In addition, the Arduino platform accepts many additional sensors, infra-red range detectors, magnetometers, temperature sensors, etc. Construction is simple, 10-20 minutes with no electrical expertise required.  Robot bodies are highly interchangeable, making it easy to construct robots for different applications: indoors, outdoors, low speed, high speed, unstructured terrain, multi-agent swarms, etc.
The tutorial will cover three topics.  1) How to build the robots.  2) How to program the robots, including leveraging powerful existing libraries such as voice recognition and image processing.  3) A discussion of potential domains for EC research.

As part of the tutorial we will demonstrate how to build a COTSBot, and will demonstrate a variety of current projects, including teleoperation, inter-bot communications, cooperative and on-line EC based learning.  Finally, we will have several COTSBots on-hand for participants to try out.

Terence Soule
He is an Associate Professor in the Computer Science Department at the University of Idaho.  He has been active in the field of Evolutionary Computation for 15 years, with over 50 publications.  He is an associate editor for the journal Genetic Programming and Evolvable Machines and was Editor in Chief for GECCO in 2012.

 

 

 


Industrial Applications of Evolutionary Algorithms

The increasing complexity of products and processes leads directly to the growing intricacy of the problems and issues the industrial world is facing. More and more often, traditional computational techniques prove unable to cope with real world situations, either because the time needed to reach an optimal solution is not compatible with the frantic development processes of a company, or because the modeling of complex systems to the degree of precision needed is unfeasible. Over the course of the past four decades, Evolutionary Algorithms (EAs) demonstrated their effectiveness in an extended variety of problems, ranging from airfoil design to credit card fraud detection.

The industrial world is starting to introduce these powerful techniques into real procedures, and nowadays experts with deep knowledge of both EAs and modern industrial needs are of paramount importance. This tutorial presents different case studies of EAs successfully applied to real world problems, showing the untapped potential of these techniques in various industrial fields. Case studies will be illustrated showing the practical problems involved, and how they were solved.

The tutorial would be organized in three main sections: the first groups industrial problems related to the verification of hardware and software working prototypes; the second presents a collection of real-world case studies pertaining reliability; the third section introduces results obtained through the application of EAs to test generation problems for hardware and circuits.

The tutorial could be interesting for students and practitioners interested in practical applications, and it does not require advanced skills in EC.

NOTE: With Alberto Tonda and Ernesto Sanchez, we recently published a book with Springer on this topic ("Industrial Applications of Evolutionary Algorithms", Intelligent Systems Reference Library, Vol. 34, ISBN 978-3-642-27467-1).

Giovanni Squillero 
He received his M.S. and Ph.D. degrees in computer science engineering, respectively, in 1996 and 2000, from Politecnico di Torino, Torino, Italy, and is presently an Assistant Professor at the same institution. In retrospect, the recurring theme of Squillero's research activities is the exploitation of bio-inspired techniques for tackling real (or at least "realistic") problems, usually in direct collaboration with industries. In mid 1990s, he started combining evolutionary techniques with verification and testing of electronic circuits. In the following years, his research interests included systems described at higher levels, with special emphasis on microprocessors and programmable devices. This also leaded to the exploration of the automatic generation of (turing complete) assembly programs. The research eventually broadened including different types of system verification (eg. software in cellular phones) and different bio-inspired optimizations (eg. drift minimization in electronic noses). Squillero is the author of 3 books (one didactic); 15 journal articles; 9 book chapters and 96 papers in conference proceedings. He used to organize the Workshop on Hardware optimization Techniques at EvoSTAR, and  (in 2011 and 2012) served as track chair for ALIFE at GECCO.

 

 

 


Computational Intelligence and Games

Games are a rather young but fast growing application domain where methods of computational intelligence, and thus evolutionary computation, are widely applied. This tutorial gives a brief overview of the area and highlights the several research and application opportunities for computational intelligence and evolutionary computation methods. It also provides in-depth perspective on specific classes of problems where evolutionary computation has proved very successful. In particular, it covers the automatic/assisted design of game intelligence (NPC), the procedural generation of game contents (PCG), interactive evolution of personalized content, etc.

Daniele Loiacono
He is an assistant professor at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of Politecnico di Milano, where he also received his Ph.D. in 2008.
His research interests include machine learning, evolutionary computation, and computational intelligence in games.
Since 2008, he has been organizing several scientific games-related competitions at major conferences including GECCO, CEC and CIG.

 

Mike Preuss:
He is Research Associate at the Computer Science Department, TU Dortmund, Germany, where he also received his Diploma degree in 1998.
His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization and the experimental methodology for (non-deterministic) optimization algorithms. He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games, pushing forward modern approaches of experimental analysis as the Exploratory Landscape Analysis (ELA) and innovative uses of surrogate models. He was involved in founding the EvoGames track at Evo* and the Digital Entertainment Technologies and Arts (DETA) track at GECCO. Within the games field, he is mainly interested in AI for realtime strategy (RTS) games and procedural content generation (PCG).

 

 

 


How to create meaningful and generalizable results

CI methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control.

Tools, which determine the applicability of CI methods in these application domains in an objective manner, are lacking. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state-of-the-art. However, there are several drawbacks of these test suites, namely:

  • problem instances are somehow artificial and have no direct link to real-world settings;
  • since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions. As a consequence, studies (benchmarks) provide insight into how these algorithms perform on this specific set of test instances, but no insight is gained in how they perform in general;
  • statistical tools for comparing several algorithms on different test problem instances are relatively complex and results are not easy to analyze.

We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models, see, e.g. [Chia10a].
This tutorial combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments. This framework extends ideas presented in [Bart11g].
The generalization of results from multi-objective optimization problems will also be addressed.

Thomas Bartz-Beielstein
He is a professor for Applied Mathematics at Cologne University of Applied Sciences (CUAS). He is head of the research center CIplus (Computational Intelligence) at CUAS. His expertise lies in optimization, simulation, and statistical analysis of complex real-world problems. Thomas has more than 100 publications on computational intelligence, optimization, simulation, and experimental research.

 

 

 

Boris Naujoks
He is one of the leading scientists on evolutionary multi-objective optimization in Germany. He received his PhD from the CI research group in Dortmund, where he focused on industrial applications of evolutionary algorithms at an early stage. He managed different projects in applying evolutionary multi-objective optimization techniques in different real-world applications from airfoil design in aerospace industry to vehicle routing problems in logistics.

 

 

 

M. Eng. Martin Zaefferer
He is a PhD student at CUAS. From 2010 until 2012 he was a master student of Engineering in Automation & IT. He studied electrical engineering with a focus on automation at CUAS and graduated in 2010 (diploma). His research interests include computational intelligence, applications of knowledge discovery and sequential parameter optimization. Martin is an experienced programmer with a strong background in R and Matlab.

Further information about the presenters can be found on http://www.spotseven.org

 


Computational Aesthetic Evaluation: Automated Fitness Functions for Evolutionary Art, Design, and Music

Current generative systems for computer art and music can blindly create works, but typically lack a critical capacity allowing automated aesthetic evaluation and feedback. Evolutionary systems for art, design, and music are particularly impacted in that the artist/programmer must remain in the loop manually selecting aesthetic winners and losers in the genetic competition. These interactive evolutionary systems suffer from what has been called “the fitness bottleneck.” Lacking the rather straight forward optimization oriented fitness functions found in industrial applications, the success of evolutionary art, design, and music has been muted.

This tutorial provides a fast moving state-of-the-art overview of computational aesthetic evaluation for automated fitness functions. Some notable limited successes aside, computational aesthetic evaluation is far from a solved problem, and a "how to" tutorial is not possible at this time. The intent of this tutorial is to identify all of the significant trail heads, to share what previous explorers have found, and to encourage future journeys by artists, programmers, and researchers down paths that seem promising.

We begin with a quick presentation of terminology, noting aspects of critical evaluation outside the scope of aesthetics and this course. Next we cover classic formulaic and geometric theories of aesthetics possibly amenable to digital exploitation. These include Birkhoff's "aesthetic measure," the golden ratio, Zipf's law, fractal dimension, as well as basic gestalt design principles and the rule of thirds.

A significant presentation of specific evolutionary art systems follows with close attention to aesthetic evaluation in fitness functions. Techniques include interactive systems, strategies for automated evaluation such as performance goals, error measures, complexity measures, and multi-objective and Pareto optimization. In addition we will explore biologically inspired methods that produce emergent aesthetic fitness functions such as coevolution, niche construction, swarm behavior, and curious agents.

Finally we will explore what might well lead to future robust fitness functions for evolutionary art; psychological models of aesthetics, and the nascent field of neuroaesthetics. Along with reviewing some specific empirical studies, we will cover topics such as Rudolf Arnheim’s application of the law of prägnanz and gestalt principles in perception; Daniel Berlyne’s notion of arousal potential; Colin Martindale’s neural network-based prototypicality model as well as his “clockwork muse” theory of stylistic change; neuroaesthetic phenomenon such as peak shift and habituation; and Jeff Hawkin’s hierarchical temporal memory programming approach inspired by the architecture of the neocortex portion of the brain.

 

Philip Galanter
He is an artist, theorist, curator, and an Assistant Professor at Texas A&M University conducting graduate studios in generative art and physical computing. Philip creates generative hardware systems, video and sound art installations, digital fine art prints, and light-box transparencies. His work has been shown in the United States, Canada, Tunisia, Italy, the Netherlands, and Peru.
Philip’s research includes the artistic exploration of complex systems, and the development of art theory bridging the cultures of science and the humanities. His writing has appeared in both art and science publications. Recent publications have focused on computational aesthetic evaluation and neuroaesthetics.  As a curator Philip collaborated with Douglas Repetto to create the first ArtBots exhibits in 2002 and 2003, with coverage by CNN, NBC, NPR, the New York Times, Wired, and Nature. He collaborated with Ellen Levy to create COMPLEXITY, the first traveling fine art museum exhibition focused on complex systems

 

 

 


Open Issues in Genetic Programming

 It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP.

These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this tutorial we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.

The tutorial is strongly inspired by a paper published by us in the Genetic Programming and Evolvable Machines journal, issue 11, pages 339–363, in March 2010 (DOI 10.1007/s10710-010-9113-2).

Nevertheless, the presentation will not be limited to the contents of that paper, also discussing new stimulating ideas and promising findings that were developed by GP researchers in the last two years.

M. Oneill

Leonardo Vanneschi
He is an Assistant Professor at the ISEGI (Institute of Statistics and Information Management) of the Nova University of Lisbon (Portugal). He holds a Master ("Laurea") Degree in Computer Science from the University of Pisa (Italy) and a PhD in Computer Science from the University of Lausanne (Switzerland). His research interests lie in the foundations and application of Evolutionary Algorithms, in particular Genetic Programming (GP), other heuristic search methods like Particle Swarm Optimization (PSO), Machine Learning, with emphasis on automated heuristic design and fitness landscape analysis, and Complex Systems modelling. Among his research contributions are the study and definition of indicators of problem hardness for GP based on fitness landscapes and the definition of several variants and improvements of the standard algorithms of GP and PSO, with emphasis on parallel and distributed versions. He has also worked on several applications, in particular concerning the study of pharmacokinetic parameters for drug discovery and development and the research for personalized therapies for some particular diseases. In the last months, his interest addressed in particular the study of semantic genetic operators in GP and a project on this subject that has Leonardo Vanneschi as the principal investigator  has recently been financed by the FCT (Funds for Science and Technology), Portugal.

 

Steven Gustafson
He leads the Knowledge Discovery Lab at the General Electric Global Research Center in Niskayuna, New York. The Knowledge Discovery Lab is focused on large-scale data, semantics, ontologies and text mining, and pattern search and discovery. As a former member of the Machine Learning Lab and Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a member of several program committees, several journal editorial boards, and a Technical Editor-in-Chief of the journal Memetic Computing. In 2006, he received the IEEE Intelligent System's ³AI¹s 10 to Watch² award.

W. Banzhaf

 

 

 


Call For Tutorial Proposals

** TUTORIAL PROPOSALS **
GECCO Tutorials will be presented by domain experts to cover current topics relevant to evolutionary computation researchers and practitioners. Each tutorial will be 110-minutes long; therefore, we encourage the inclusion of interactive activities and demos.

Tutorials will be free to all GECCO 2013 attendees. Tutorial instructors will receive free registration for a new tutorial, and half-registration fees for a repeating tutorial. It is expected that all the instructors involved in a tutorial will attend the conference. Accepted tutorial's slide sets will be collected by Sheridan/ACM Press, and published along with the conference proceedings and the ACM digital library.

** SUBMISSION PROCESS **
Each tutorial/workshop proposal should include:

1. A half-page extended abstract (in plain text) that includes: the tutorial/workshop title, the name and affiliation of the instructor(s), and a description of scope and content.
2. Short bio of the instructor(s) (about half-page in plain text).
3. [Highly encouraged] A description of any interactive activity or demo planned within the tutorial presentation/workshop.

** REVIEWING **
Tutorial proposals will be reviewed by the GECCO 2013 organizing committee, based on the GECCO attendees' likely interest in them, the breadth and depth of the topic(s), and the expertise and credentials of the instructor(s)/organiser(s).

  Tutorials Information and Submissions: Gabriela Ochoa

   Workshops Information and Submissions: Mike Preuss


 

 

 
 
  

Introductory Tutorials
Advanced Tutorials
Specialized Techniques and Applications

IMPORTANT DATES

Deadline for Proposals: November 07th, 2012
Notification of Acceptance: November 30th, 2012

 

 
 
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