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: 
UnaMay ’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  
ModelBased
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 userfriendly 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 CMAES (Covariance Matrix Adaptation)
more info: 
Anne Auger 
Nikolaus Hansen  
ConstraintHandling
Techniques used with Evolutionary Algorithms
more info: 
Carlos CoelloCoello  
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  
BlackBox
Complexity: From Complexity Theory to Playing Mastermind
more info: 
Benjamin Doerr

Carola Doerr  
Advances
on Evolutionary Manyobjective 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 GeneticsBased 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 LópezIbáñez 
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 BartzBeielstein 
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 realworld 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 cofounder and VP Technology of Red Cedar Technology, which provides tools for automated engineering design based on evolutionary computation. He chaired ICGA97 and GECCO2001, chaired GECCO's sponsoring organization, ISGEC, from 20012004, and was the founding chair of ACM SIGEVO, 20052007.




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. parsetree, linear and graphbased variation operators that preserve syntax and explore a variable length, hierarchical solution space
• appropriately chosen programming functions
• fitness function specification

UnaMayO'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 schemabased 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 (multicore compilers and cloud virtualization technology)
Dr. O'Reilly developed a genetic programming based design system named Genr8 that is opensource and used throughout the world by researchers in Architecture. As VP of Technology at a Cambridge consulting firm, she led the development of a webbased 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 SIGEVO 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 coedited 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 selfadaptation 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 selfadaptation mutation variances
extensively. With both mutation and recombination, the (m,l)strategy is a
well known and understood algorithm for mixedinteger optimization (including
the integeronly and the realvaluedonly cases).
The tutorial will present all basic algorithmic variants as well
as guidelines for their implementation. Moreover, extensions such
as mixedinteger 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 datadriven 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 NPhard 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 experiencebased 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 editorinchief 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 socalled Paretooptimal 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 setbased algorithms, they allow to compute or approximate the full set of Paretooptimal solutions in one algorithm runopposed to classical solutionbased 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 (Paretooptimal) solutions (innovization) and it has been shown that certain singleobjective 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 solutionbased 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 stateoftheart algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicatorbased 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 stateoftheart 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 positionsfirst at INRIA Saclay IledeFrance 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 manyobjective optimization, benchmarking, and theoretical aspects of indicatorbased 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 offtheshelf 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 genotypephenotype 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 representationoperator combinations on EA performance.
These concepts are
• locality and
• redundancy.
Locality is a result of the interplay between the search operator and the genotypephenotype 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, ebusiness, and software
engineering, coedited 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, coorganizer of
the European workshop series on "Evolutionary Computation in Communications,
Networks, and Connected Systems", coorganizer of the European workshop
series on "Evolutionary Computation in Transportation and Logistics", and
cochair 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. valuefunction 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 fixedtopology 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.


ModelBased Evolutionary Algorithms
In modelbuilding evolutionary algorithms the variation operators are guided by the use of a model that conveys as much problemspecific 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. Wellknown types of algorithms of the latter type are EstimationofDistribution 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 blackbox 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 cochaired 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 cochair of the program committee of the Genetic Algorithm track in GECCO2004 and GECCO2006, and EditorinChief of GECCO2007.


Peter A. N. Bosman
He
is a senior scientist in the research group
Multiagent 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
estimationofdistribution 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), nonparametric statistics, and will touch on multivariate and multifactorial 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 userfriendly 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 userfriendly 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 userfriendly 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 cotrack chair for the GeneticsBased 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
/ realworld 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 Cotrack chair for GeneticsBased Machine Learning in Genetic and Evolutionary Computation Conference 2011 and 2012 [plus organizing committee of the International Workshop on Learning Classifier Systems (IWLCS) from 20092010]. 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 [2004present], Congress on Evolutionary Computation [2004present], Hybrid Intelligent Systems [2003present], Parallel Problem Solving from Nature [2004present] 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 20102012 and is returning as an organizer from 20122014.




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 wellknown and important example problems.
Through a careful and thorough introduction of important analytical tools and methods,
including fitnessbased 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 nontrivial 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 customtailored 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
NPHard 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 coorganised ThRaSH 2009 and chaired the "Randomized Search Heuristics:
Theoretical Aspects and Real World Applications" minisymposium at the 2011 Siam Conference on
Optimization (SIAMOPT 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 natureinspired search heuristics, in
particular runtime analysis of populationbased evolutionary algorithms.



Advanced Tutorials

Evolution Strategies and CMAES (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 stepsize control are reviewed, onefifth
success rule and path length control. Then, Covariance Matrix
Adaptation (CMA) is discussed in depth: rankone update, the
evolution path, rankmu update. Invariance properties and the
interpretation as natural gradient descent are touched upon.
In the beginning, general difficulties in solving nonlinear,
nonconvex optimization problems in continuous domain are revealed,
for example nonseparability, illconditioning and ruggedness.
Algorithmic design aspects are related to these difficulties. In the
end, the performance of the CMAES is related to other wellknown
evolutionary and nonevolutionary 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 (20042006) 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 CMAES over many years. 

ConstraintHandling 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 CINVESTAVIPN in
Mexico City, Mexico.
He has published over 320 papers in international
peerreviewed journals, book chapters, and conferences.
He has also coauthored the book "Evolutionary
Algorithms for Solving MultiObjective Problems",
which is now in its Second Edition (Springer, 2007)
and has coedited the book "Applications of
MultiObjective 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 multiobjective
optimization and constrainthandling 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 constrainthandling
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 MAXkSAT and NKLandscapes 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 wellknown combinatorial optimization problems. These methods are being used to develop new search strategies for MAXSAT and NKLandscapes 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 EditorinChief 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 wellknown problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical singleobjective 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 singleobjective 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 ChristianAlbrechtsUniversity 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 multiobjective 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 coorganized the biannual Dagstuhl seminar "Theory of
Evolutionary Algorithms" in 2008 and 2010 and has given tutorials on
the computational complexity of bioinspired 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 commonlyused 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 adaptivewalk 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 SophiaAntipolis, France, since 2006. He received a PhD in computer science from the University of Nice SophiaAntipolis, 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 IledeFrance, 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.


BlackBox 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, blackbox complexitya
notion suggested by Droste, Jansen, and Wegener in their seminal 2006
Theory of Computing Systems paperhas gained more and more attention.
Many deep results have been developed, among others 2 GECCO best paper
awards.
The blackbox complexity of a problem, in simple words, is the minimum
number of fitness evaluations needed by any algorithm to solve it.
Comparing the blackbox 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 blackbox
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 blackbox complexity, we survey the recent progress on this topic, and we discuss several directions for future research. Since one of the most basic blackbox 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 problemspecific
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 blackbox
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 cochair 20072009. 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 postdoc 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
blackbox 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 Manyobjective Optimization
Multiobjective evolutionary algorithms (MOEAs) are widely used in practice for solving multiobjective 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 manyobjective 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 manyobjective optimization and scale up poorly with the number of objectives of the problem. The development of robust, scalable, manyobjective 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 manyobjective landscapes and the interaction between selection, variation, and population size to appropriately support the evolutionary search in highdimensional spaces.
This tutorial aims at giving an introduction to evolutionary manyobjective optimization, discussing important characteristics of manyobjective 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 multiobjective evolutionary algorithms, (ii) show scalability issues of conventional multiobjective optimizers when applied to manyobjective problems, (iii) introduce important features of manyobjective landscapes, (iv) show the effectiveness of selection and variation operators when the characteristics of the manyobjective landscapes are taken into account, (v) show the effects of population size, (vi) present a general overview of the approaches to manyobjective optimization, together with their stateoftheart 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 manyobjective (anyobjective) 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 manyobjective evolutionary algorithms.


Evolutionary Computation for Dynamic Optimization Problems (ECDOP)
Many realworld 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 decisionmaking
due to their nature of difficulty. Yet, they are important problems that decisionmakers 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 wellunderstood.
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 stateoftheart 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 (TFINS) 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 cochair of the IEEE Symposium on
CI in Dynamic and Uncertain Environments. He has been on the program committee for over 40 conferences. He
has coedited 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 stateoftheart EC methods for DOPs. Both the
benchmark generators and stateoftheart 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 problemsolving 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 Pushbased research, including the production of humancompetitive 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 EditorinChief 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 selfmodification operators (selfmodifying 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, nbit 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 cochair 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
20022003 and was Genetic Programming track chair in
2008. He was cochair 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 GeneticsBased Machine Learning
We are living in the petabyte 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 humanunderstandable solutions are key on several domain areas. Geneticsbased 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 largescale 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 largescale 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 coedited two books. From 2007 to 2010 he was the coorganizer
of the International Workshop on Learning Classifier Systems and in 2009 and 2013
he is/was the (co)chair the GeneticsBased Machine Learning track of GECCO. His work
won the bestpaper award of the GeneticsBased 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 selfcontained. 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 PostDoc 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 cotrack chair of the Genetic Algorithm track
of GECCO 2013, was program chair at PPSN 2008, coorganised 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 cofounder and former track cochair of the GDS track at GECCO. He is also a cofounder and the editorinchief 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 fixedsize realvalued 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 nonregular representations (e.g. variablelength) or handling some nonconventional evaluation methods that are hardly usable by other methods (e.g. strongly nonconvex optimization, multiobjective 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, holdout and crossvalidation methods in EC, members selection method for making ensembles, diversity production with EC for ensemble methods, and optimization with nonconventional evaluation methods (e.g. AUCROC).

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 20052006 period, he was ERCIM postdoctoral fellow jointly at the INRIA SaclayIledeFrance in Orsay (France) and the University of Lausanne (Switzerland). He also worked as research analyst for Informatique WGZ (20062007) 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, coevolution, geneticbased machine learning), machine learning (reinforcement learning, ensemble methods, pattern recognition), and distributed computing (sensor networks, autonomic computing, highperformance 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 DEvariants perturb the currentgeneration 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
singleobjective optimization. Application of the DE family of algorithms for multiobjective,
constrained, largescale, dynamic and multimodal 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 stateoftheart DE algorithms and
demand strong research effort from the DEcommunity in the future. The talk will also touch on
other population based realparameter optimization algorithms, characteristic distinctions of
these algorithms with respect to DE and performance comparison among these realparameter
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 coeditorinchief of Swarm and Evolutionary Computation, an Elsevier Journal. His coauthored SaDE (April 2009) paper won "IEEE
Trans. on Evolutionary Computation" outstanding paper award. His research interests include
evolutionary computation, pattern recognition, multiobjective 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
timeoptimal 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
multiobjective 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 postdoc 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, Multiobjective Optimization,
Decision Making and Evolutionary Algorithms. 

Automatic (Offline) Configuration of Algorithms
Most optimization algorithms, including evolutionary algorithms and
metaheuristics, and generalpurpose 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 trialanderror 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 multiobjective 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 stepbystep guide on using irace for the typical algorithm
configuration scenario.

Manuel LópezIbáñez
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, multiobjective
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 multiobjective
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 coauthor 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, multiobjective 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 Frace, Iterated Frace 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 online and onboard evolutionary learning, cooperative learning, and cooperative behaviors and include handson 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), featurerich, robot with the computational power to support research in evolutionary robotics including onboard and online evolution. The robots use modern development environments and have builtin, software accessible web cameras, wireless and Bluetooth capabilities, accelerometers, GPS, microphones, speakers, etc. In addition, the Arduino platform accepts many additional sensors, infrared range detectors, magnetometers, temperature sensors, etc. Construction is simple, 1020 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, multiagent 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, interbot communications, cooperative and online EC based learning. Finally, we will have several COTSBots onhand 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 realworld 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 9783642274671).

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 bioinspired 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 bioinspired 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 indepth 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 gamesrelated 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 realvalued problems, namely on multimodal and multiobjective optimization and the experimental methodology for (nondeterministic) 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 realworld 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 stateoftheart. However, there are several drawbacks of these test suites, namely:
 problem instances are somehow artificial and have no direct link to realworld 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 multiobjective optimization problems will also be addressed.

Thomas BartzBeielstein
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 realworld 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 multiobjective 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 multiobjective optimization techniques in different realworld 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 stateoftheart 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 multiobjective 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 networkbased 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 lightbox 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 realworld 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/s1071001091132).
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 largescale 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 EditorinChief 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 110minutes 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
halfregistration 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 halfpage 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 halfpage 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
