In 2009, GECCO will be held WEDNESDAY to SUNDAY, not the traditional Saturday through Wednesday. However, WORKSHOPS and TUTORIALS will still take place on the first two days of the conference, Wednesday and Thursday. See schedule

Free Tutorials and Workshops:
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.

Proceedings will be published and distributed to all registered attendees.

To propose a tutorial, contact Martin V. Butz at

Planned Free Tutorials

Introductory

 

Genetic Algorithms
more info:

Erik Goodman   -   
Genetic Programming
more info:
Riccardo Poli   -   
Nic McPhee   -   
Evolution Strategies: Basic Introduction
more info:
Thomas Bäck   -   
Evolutionary Computation: A Unified Approach
more info:
Kenneth De Jong   -   
Financial Evolutionary Computation
more info:
Christopher D. Clack   -   
Ant Colony Optimization
more info:
Christian Blum   -   
Learning Classifier Systems
more info:
Pier Luca Lanzi   -   
Probabilistic Model-Building GAs
more info:
Martin Pelikan   -   
Grammatical Evolution
Conor Ryan   -   
Statistical Analysis for Evolutionary Computation: Introduction
Mark Wineberg   -   
Steffen Christensen   -   
Evolutionary Neural Networks
more info:
Risto Miikkulainen   -    
Kenneth O. Stanley   -   


Advanced

 
GA Theory
Jonathan Rowe   -   
GP Theory I&II
more info:
William B. Langdon   -   
Riccardo Poli   -   
No Free Lunch
Darrell Whitley   -   
Bioinformatics
more info:
Jason Moore   -   
Evolutionary Multiobjective Optimization
more info:
Eckart Zitzler   -   
Constraint Handling Techniques Used with EAs
more info:
Carlos Coello Coello   -   
Statistical Analysis for Evolutionary Computation: Advanced Techniques
Mark Wineberg   -   
Steffen Christensen   -   
Representations for Evolutionary Algorithms
more info:
Franz Rothlauf   -   
Computational Complexity and EC
more info:
Thomas Jansen   -   
Frank Neumann   -   

Experimental Research in EC
more info:

Mike Preuss   -   
Thomas Bartz-Beielstein   -   
Elementary Landscape Analysis for TSP, Graph Coloring, Graph Partitioning, and MAXSAT
Andrew Sutton   -   
Darrell Whitley   -   


Specialized Techniques and Applications

 
A Billion Bits or Bust: Little Models and Efficiency Enhancement for Fast, Effective Genetic Algorithms on Extremely Large, Hard Problems.
Kumara Sastry  -   
Accelerating Evolutionary Computation with Graphics Processing Units
more info:
Wolfgang Banzhaf   -   
Simon Harding     -     
Evolving Quantum Computer Algorithms
more info:
Lee Spector   -   
Symbolic Regression
Maarten Keijzer   -   
An Information Perspective on Evolutionary Computation
Yossi Borenstein   -  
Generative and Developmental Systems
more info:
Kenneth O. Stanley   -   
(Genetic and) Evolutionary Computer Vision
more info:
Mengjie Zhang   -   
Stefano Cagnoni   -   
Gustavo Olague   -   
Evolutionary Design
Ian Parmee   -   
Large scale data mining using Genetics-Based Machine Learning
more info:
Jaume Bacardit   -   
Xavier Llorà   -   
Evolutionary Multiobjective Combinatorial Optimization
more info:
Rajeev Kumar   -   
Applications of Evolutionary Neural Networks
Risto Miikkulainen   -   
Kenneth O. Stanley   -   
Synthetic Biology II: Modeling & Optimization
more info:
Natalio Krasnogor   -   
Experimental Optimization by Evolutionary Algorithms
more info:
Thomas Bäck   -   
Ofer M. Shir   -    
Synthetic Biology I: Fundamentals & Devices
more info:
Nawwaf Kharma   -   
Luc Varin     -     
Cartesian Genetic Programming
more info:
Julian F. Miller   -   
Simon Harding    -   
Bio-inspired Telecommunications
more info:
Muddassar Farooq  -  
Theory of Randomized Search Heuristics in Combinatorial Optimization
more info:
Carsten Witt    -   
Fitness Landscapes and Graphs: Multimodularity, Ruggedness and Neutrality
more info:
Sébastien Verel   -   
Evolution Strategies and Covariance Matrix Adaptation
Anne Auger    -   
Nikolaus Hansen    -   
Fitness Landscapes and Problem Hardness in Genetic Programming
more info:
Leonardo Vanneschi   -   

 


Introductory

 

Genetic Algorithms:


Description of Tutorial

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

Biosketch:

Erik Goodman is a Professor of Electrical and Computer Engineering and of Mechanical Engineering at Michigan State University. He studied genetic algorithms under John Holland at the University of Michigan, before they had been named. His use of a genetic algorithm in 1971 to solve for 40 coefficients of a highly nonlinear model of a bacterial cell was the first known GA application on a real-world problem, and ran for nearly a year on a dedicated computer. He has used genetic algorithms in his research 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 ligand docking, for design of shape and layup of composite structures, and for data mining and pattern classification.

Much of his recent research has been in development of new types of parallel genetic algorithms and in design methods for mechatronic systems using genetic programming. He is co-founder and VP Technology of Red Cedar Technology, which provides tools for automated engineering design based on evolutionary computation. He chaired ICGA-97 and GECCO-2001, chaired GECCO’s sponsoring organization, ISGEC, from 2001-2004, and is now chair of ACM SIGEVO, its successor.


Genetic Programming:


Description of Tutorial

Genetic programming (GP) is a collection of evolutionary computation techniques that allow computers to solve problems automatically. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance.

Since its inception twenty years ago, GP has been used to solve a wide range of practical problems, producing a number of human-competitive results and even patentable new inventions. Like many other areas of computer science, GP is evolving rapidly, with new ideas, techniques and applications being constantly proposed, making it is difficult for people to identify the key ideas in the field and keep up with the pace of new developments. The aim of this tutorial is to provide a roadmap to GP for both newcomers and old-timers.

The tutorial starts with a gentle introduction which describes how a population of programs is stored in the computer so that they can evolve with time. We explain how programs are represented, how random programs are initially created, and how GP creates a new generation by mutating the better existing programs or combining pairs of good parent programs to produce offspring programs. Then, we describe a variety of alternative representations for programs and some advanced GP techniques. These include: the evolution of machine-code and parallel programs, the use of grammars and probability distributions for the generation of programs, variants of GP which allow the solution of problems with multiple objectives, many speed-up techniques and some useful theoretical tools. To illustrate genetic programming's scope, we then review some real-world applications of GP. The tutorial is concluded by a series of recommendations and suggestions to obtain the most from a GP system and open questions.



Riccardo Poli

Biosketches:

Riccardo Poli
is a Professor in the School of Computer Science and Electronic Engineering at Essex. He started his academic career as an electronic engineer doing a PhD in biomedical image analysis to later become an expert in the field of EC. He has published around 250 refereed papers and two books on the theory and applications of genetic programming, evolutionary algorithms, particle swarm optimisation, biomedical engineering, brain-computer interfaces, neural networks, image/signal processing, biology and psychology. He is a Fellow of the International Society for Genetic and Evolutionary Computation (since 2003), a recipient of the EvoStar award for outstanding contributions to this field (2007), and an ACM SIGEVO executive board member (2007-2013).

He was co-founder and co-chair of the European Conference on GP (1998-2000, 2003). He was general chair (2004), track chair (2002, 2007), business committee member (2005), and competition chair (2006) of ACM's Genetic and Evolutionary Computation Conference, co-chair of the Foundations of Genetic Algorithms Workshop (2002) and technical chair of the International Workshop on Ant Colony Optimisation and Swarm Intelligence (2006). He is an associate editor of Genetic Programming and Evolvable Machines, Evolutionary Computation and the International Journal of Computational Intelligence Research. He is an advisory board member of the Journal on Artificial Evolution and Applications and an editorial board member of Swarm Intelligence. He is a member of the EPSRC Peer Review College, an EU expert evaluator and a grant-proposal referee for Irish, Swiss and Italian funding bodies.


Nicholas Freitag McPhee is a Full Professor in Computer Science in the Division of Science and Mathematics, University of Minnesota, Morris. He is an associate editor of the Journal on Artificial Evolution and Applications, an editorial board member of Genetic Programming and Evolvable Machines, and has served on the program committees for dozens of international events. He has extensive expertise in the design of GP systems, and in the theoretical analysis of their behaviours. His joint work with Poli on the theoretical analysis of GP received the best paper award at the 2001 European Conference on Genetic Programming, and several of his other foundational studies continue to be widely cited.


Nicholas McPhee

He has also worked closely with biologists on a number of projects, building individual-based models to illuminate genetic interactions and changes in the genotypic and phenotypic diversity of populations.

 


Evolution Strategies: Basic Introduction:


Description of Tutorial

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

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

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

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

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

Thomas Bäck, PhD, 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.


Thomas Bäck

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


Evolutionary Computation: A Unified Approach:


Description of Tutorial

The field of Evolutionary Computation (EC) has experienced tremendous growth over the past 15 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 se 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 EC via a general framework that can help compare and contrast approaches, encourage crossbreeding, and facilitate 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

Biosketch:

Kenneth A. De Jong 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 genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as heuristics for NP-hard problems, and the application of EAs to the problem of learning task programs in domains such as robotics, diagnostics, navigation and game playing.

He is also interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly their control or the control of a benevolent teacher. Support for these projects is provided by DARPA, ONR, and NRL. He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of the ACM SIGEVO. He is the recipient of an IEEE Pioneer award in the field of Evolutionary Computation and a lifetime achievement award from the Evolutionary Programming Society.


Financial Evolutionary Computing:

Description of Tutorial

Financial investment and trading have been transformed through the application of mathematical analysis and computer technology. The research problems posed by financial computing are extremely challenging, taxing both mathematicians and computer scientists. While traditional computational techniques have yet to provide an efficient means for numerical evaluation of the complex equations produced by financial mathematicians, evolutionary computing has been remarkably effective and Financial Evolutionary Computing is currently a fertile area of research.

The Introduction to Financial Evolutionary Computing (FEC) Tutorial is aimed at GECCO attendees with limited knowledge of finance. The tutorial will introduce the area of FEC, provide a basic understanding of trading and investment, identify some of the main research challenges, who is working in the area, and how to get started on FEC research. Topics will include for example stock selection, calculation of value at risk, and modelling financial markets.


Christopher D. Clack

Biosketch:

Christopher D. Clack is Director of Financial Computing at UCL. He founded UCL's Virtual Trading Floor and has attracted funds exceeding £ 2.1 million in the last three years. He is Coordinator of the PROFIT European Network in Financial Computing, which includes UCL, Deutsche Bank, Reuters, and the universities of Athens, Moscow, Prague, and Sofia. He was conference chair at Trade Tech Architecture 2008, is a regular panel member at both industry and academic conferences and workshops, and is also presenting a tutorial on Financial Evolutionary Computing at GECCO 2008.

His research team focuses on applying Genetic Computation and multi-agent systems to real-world problems in finance, and his work on GP robustness in highly volatile markets won a Best Paper award at GECCO 2007. He has twenty years' experience of consulting in Financial Computing, from settlement systems to portfolio optimization, and has established very close partnerships with Credit Suisse, Goldman Sachs, Merrill Lynch, Morgan Stanley, Reuters, and the London Stock Exchange.This provides unrivalled exposure to the most pressing technology problems in finance, coupled with invaluable access to real data.


Ant Colony Optimization:

Description of Tutorial

Ant colony optimization (ACO) is an approximate method for tackling combinatorial as well as continuous optimization problems. From an artificial intelligence point of view ACO is a swarm intelligence method, while from the operations research point of view ACO belongs to the class of algorithms known as metaheuristics. The inspiring source of ACO is the foraging behaviour of ant colonies. The aims of this tutorial are twofold. First, we explain the basics of ACO algorithms in order to satisfy participants that have no previous knowledge of this technique. Second, we also deal with more advanced features of ACO with the aim of presenting useful material for participants with some prior knowledge of ACO algorithms.

The tutorial starts by introducting the swarm intelligence origins of ACO algorithms. Then, it is shown how to translate the biological inspiration into a technical algorithm. Some of the most successful ACO variants are presented in this context. More advanced features of ACO algorithms are presented in the context of appealing applications. Finally, the tutorial concludes with an interesting recent research topic: the hybridization of ACO algorithms with other optimization techniques.


Christian Blum

Biosketch:

Christian Blum received the doctoral degree in 2004 from the Free University of Brussels (Belgium). After spending one year at the Advanced Computation Laboratory of the Imperial Cancer Research Fund (ICRF) in London, he worked at IRIDIA at the Free University of Brussels under the supervision of Marco Dorigo. He currently is a "Ramon y Cajal" research fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya in Barcelona. Current subject of his research is the use of swarm intelligence techniques for the management of large-scale mobile ad-hoc and sensor networks, as well as the hybridization of metaheuristics with more classical artificial intelligence and operations research methods.


He is a member of the editorial boards of international journals such as Computers & Operations Research and Swarm Intelligence. As a co-founder, he initiated the series of International Workshops on Hybrid Metaheuristics (HM). Finally, he gave invited tutorials at conferences such as PPSN, GECCO, and HIS.

 


Introduction to Learning Classifier Systems:

Description of Tutorial

Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for use in adaptive robotics, and today also applied to effective classification and data-mining–what is a learning classifier system? How does it work? What’s the theory behind its functioning? What are the most interesting research directions? What the applications? And what the relevant open issues? This introductory tutorial tries to answer these questions. It provides a gentle introduction to learning classifier systems, it overviews the theoretical understanding we have today, the current research directions, the most interesting applications, and the open issues.


Pier Luca Lanzi

Biosketch:

Pier Luca Lanzi was born in Turin, Italy, in 1967. He received the Laurea degree in computer science from the Università degli Studi di Udine, in 1994 and the Ph.D. degree in computer and automation engineering from the Politecnico di Milano, Milan, Italy, in 1999. He is an Associate Professor with the Department of Electronics and Information, Politecnico di Milano. He is member of the Editorial Board of the Evolutionary Computation Journal and Editor of SIGEVOlution, the newsletter of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). His research areas include evolutionary computation and machine learning. He is interested in applications to data mining and autonomous agents. .

 


Probabilistic Model-Building Algorithms (PMBGAs)

Description of Tutorial

Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).

Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions 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. There are many successful applications of PMBGAs, for example, 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.

The tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.


Martin Pelikan

Biosketch:

Martin Pelikan received Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002. He is now an associate professor at the Department of Mathematics and Computer Science at the University of Missouri in St. Louis. In 2006 Pelikan founded the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). Prior to joining the University of Missouri, he worked at the Illinois Genetic Algorithms Laboratory (IlliGAL), the German National Center for Information Technology in Sankt Augustin, the Slovak University of Technology in Bratislava, and the Swiss Federal Institute of Technology (ETH) in Zurich.

Pelikan has worked as a researcher in genetic and evolutionary computation since 1995. His most important contributions to genetic and evolutionary computation are the Bayesian optimization algorithm (BOA), the hierarchical BOA (hBOA), the scalability theory for BOA and hBOA, and the efficiency enhancement techniques for BOA and hBOA. His current research focuses on extending BOA and hBOA to other problem domains, applying genetic and evolutionary algorithms to real-world problems with the focus on physics, bioinformatics and machine learning, and designing new efficiency enhancement techniques for BOA and other evolutionary algorithms.


Evolving Neural Networks

Description of Tutorial

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


Risto Miikkulainen

Biosketch:

Risto Miikkulainen 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 editor of Machine Learning, Neural Networks, IEEE Transactions on Computational Intelligence and AI in Games, Cognitive Systems Research, and IEEE Transactions on Autonomous Mental Development.

 

 


Advanced

 


Genetic Programming Theory I & II:


Description of Tutorial

We start by describing and characterising the search space explored by genetic programming (GP). We show how to compute the size of the search space. Then, we introduce some work on the distribution of functionality of the programs in the search space and indicate its scientific and practical consequences. In particular, we explain why GP can work despite the immensity of the search space it explores. Then, we show recent theory on halting probability that extends these results to forms of Turing complete GP. This indicates that Turing complete GP has a hard time solving problems unless certain measures are put in place.

Having characterised the search space, in the second part of the tutorial, we characterise GP as a search algorithm by using the schema theory. In the tutorial we introduce the basics of schema theory, explaining how one can derive an exact probabilistic description of GAs and GP based on schemata. We illustrate the lessons that can be learnt from the theory and indicate some recipes to do GP well for practitioners. These include important new results on bloat in GP and ways to cure it.

Despite its technical contents, an big effort has been made to limit the use of mathematical formulae to a minimum.



William B. Langdon

Biosketches:

William B. Langdon,
was research officer for the Central Electricity Research Laboratories and project manager and technical coordinator for Logica before becoming a prolific, internationally recognised researcher (working at UCL, Birmingham, CWI and Essex). He has written two books, edited six more, and published over 80 papers in international conferences and journals. He is the resource review editor for {Genetic Programming and Evolvable Machines and a member of the editorial board of Evolutionary Computation. He has been a co-organiser of eight international conferences and workshops, and has given nine tutorials at international conferences. He was elected ISGEC Fellow for his contributions to EC.

Dr Langdon has extensive experience designing and implementing GP systems, and is a leader in both the empirical and theoretical analysis of evolutionary systems. He also has broad experience both in industry and academic settings in biomedical engineering, drug design, and bioinformatics.


Riccardo Poli is a Professor in the School of Computer Science and Electronic Engineering at Essex. He started his academic career as an electronic engineer doing a PhD in biomedical image analysis to later become an expert in the field of EC. He has published around 250 refereed papers and two books on the theory and applications of genetic programming, evolutionary algorithms, particle swarm optimisation, biomedical engineering, brain-computer interfaces, neural networks, image/signal processing, biology and psychology. He is a Fellow of the International Society for Genetic and Evolutionary Computation (since 2003), a recipient of the EvoStar award for outstanding contributions to this field (2007), and an ACM SIGEVO executive board member (2007-2013).


Riccardo Poli

He was co-founder and co-chair of the European Conference on GP (1998-2000, 2003). He was general chair (2004), track chair (2002, 2007), business committee member (2005), and competition chair (2006) of ACM's Genetic and Evolutionary Computation Conference, co-chair of the Foundations of Genetic Algorithms Workshop (2002) and technical chair of the International Workshop on Ant Colony Optimisation and Swarm Intelligence (2006). He is an associate editor of Genetic Programming and Evolvable Machines, Evolutionary Computation and the International Journal of Computational Intelligence Research. He is an advisory board member of the Journal on Artificial Evolution and Applications and an editorial board member of Swarm Intelligence. He is a member of the EPSRC Peer Review College, an EU expert evaluator and a grant-proposal referee for Irish, Swiss and Italian funding bodies.


Bioinformatics:

Description of Tutorial

Bioinformatics is an interdisciplinary field of study that blends computer science and statistics with the biological sciences. Important objectives of bioinformatics include the storage, management and retrieval of high-dimensional biological data and the detection, characterization, and interpretation of patterns in such data. The goal of this tutorial is to provide an entry-level introduction to bioinformatics for those hoping to apply genetic and evolutionary computation to solving complex biological problems. Specific examples of how methods such as genetic programming have been applied in bioinformatics will be presented.



Jason H. Moore

Biosketch:

Jason H. Moore, Ph.D.: Dr. Moore received his B.S. in Biological Sciences from Florida State University. He then received an M.S. in Human Genetics, an M.A. in Statistics and a Ph.D. in Human Genetics from the University of Michigan. He then served as Assistant Professor of Molecular Physiology and Biophysics (1999-2003) and Associate Professor of Molecular Physiology and Biophysics with tenure (2003-2004) at Vanderbilt University. While at Vanderbilt, Dr. Moore held an endowed position as an Ingram Associate Professor of Cancer Research. He also served as Director of the Bioinformatics Core and Co-Founder and Co-Director of the Vanderbilt Advanced Computing Center for Research and Education (ACCRE).

In 2004, Dr. Moore accepted a position as the Frank Lane Research Scholar in Computational Genetics, Associate Professor of Genetics, Associate Professor of Community and Family Medicine, and Director of Bioinformatics at Dartmouth Medical School. He was promoted to Full Professor with tenure in 2008. He also holds adjunct positions in the Department of Computer Science at the University of New Hampshire, the Department of Computer Science at the University of Vermont and as an Adjunct Investigator at the Translational Genomics Research Institute (TGen) in Phoenix. Dr. Moore serves as Director of the Computational Genetics Laboratory and the Bioinformatics Shared Resource for the Norris-Cotton Cancer Center, Director of the Integrative Biology Core for the Center for Environmental Health Sciences and Founder and Director of The DISCOVERY Resource, a 500-processor parallel computer cooperatively operated for the Dartmouth community. His research has been communicated in more than 175 scientific publications and is supported by three NIH R01 grants in his name. He has previously served as Program Chair for the Bioinformatics and Computational Biology track at GECCO and as Chair of the European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO). He is also Founder and Chair of the GECCO Workshop on Open-Source Software for Genetic and Evolutionary Computation (SoftGEC).


Evolutionary Multiobjective Optimization:

Description of Tutorial

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making.

This tutorial gives an overview of evolutionary multiobjective optimization (EMO) with the focus on methods and theory. On the one hand, basic principles of multiobjective optimization are presented, and various algorithmic aspects such as fitness assignment and environmental selection are discussed in the light of state-of-the-art techniques. On the other hand, the tutorial covers several theoretical issues such as performance assessment and running-time analysis.

The presentation is aimed for both novices and users of EMO. Those without any knowledge in EMO will have adequate ideas of the procedures and their importance in computing and problem-solving tasks. Those who have been practicing EMO will also have enough ideas and materials for future research, know state-of-the-art results and techniques, and make a comparative evaluation of their research.



Eckart Zitzler

Biosketch:

Eckart Zitzler received degrees from University of Dortmund in Germany (diploma in computer science) and ETH Zurich in Switzerland (doctor of technical sciences). Since 2003, he has been Assistant Professor for Systems Optimization at the Computer Engineering and Networks Laboratory at the Department of Information Technology and Electrical Engineering of ETH Zurich, Switzerland. His research focuses on the development and investigation of nature-inspired search methods for multiobjective optimization and their application to systems analysis and systems design, mainly in Computer Engineering and Systems Biology. Dr. Zitzler was General Co-Chairman of the first three international conferences on evolutionary multi-criterion optimization (EMO 2001, EMO 2003, and EMO 2005)


Constraint-Handling Techniques used with Evolutionary Algorithms


Description of Tutorial

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

Biosketch:

Carlos Artemio Coello Coello received a BSc in Civil Engineering from the Universidad Autonoma de Chiapas in Mexico in 1991 (graduating Summa Cum Laude). Then, he was awarded a scholarship from the Mexican government to pursue graduate studies in Computer Science at Tulane University (in the USA). He received a MSc and a PhD in Computer Science in 1993 and 1996, respectively. Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor at DePauw University (in the USA). He is currently full professor at CINVESTAV-IPN in Mexico City, Mexico.

He has published over 200 papers in international peer-reviewed journals, book chapters, and conferences. He has also co-authored the book "Evolutionary Algorithms for Solving Multi-Objective Problems", which is now in its Second Edition (Springer, 2007) and has co-edited the book "Applications of Multi-Objective Evolutionary Algorithms" (World Scientific, 2004).

He has delivered invited talks, keynote speeches and tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile, Colombia, Brazil, Argentina, India, Uruguay and Mexico.

Dr. Coello has served as a technical reviewer for over 50 international journals and for more than 60 international conferences and actually serves as associate editor of the journals "IEEE Transactions on Evolutionary Computation", "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".

He is member of the Mexican Academy of Science, Senior Member of the IEEE, and member of Sigma Xi, The Scientific Research Society. He is also a member of the Council of Authors of the "International Society for Genetic and Evolutionary Computation". His current research interests are: evolutionary multiobjective optimization, constraint-handling techniques for evolutionary algorithms and evolvable hardware.

 


Representations for Evolutionary Algorithms:

Description of Tutorial

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

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

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

• locality and
• redundancy.

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

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

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



Franz Rothlauf

Biosketch:

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

He is currently full professor at the Department of Information Systems, Mainz University, Germany. He has published more than 50 technical papers in the context of evolutionary computation, co-edited several conference proceedings, and is author of the book "Representations for Genetic and Evolutionary Algorithms" which is published in a second edition in 2006.

His main research interests are problem representations for heuristic search approaches especially evolutionary algorithms. For several years he was a visiting researcher at the Illinois Genetic Algorithm Laboratory (IlliGAL). He is a member of the Editorial Board of Evolutionary Computation Journal, Information Sciences, and Journal of Artificial Evolution and Applications.
He has been organizer of several workshops on representations issues, chair of EvoWorkshops in 2005 and 2006, co-organizer of the European workshop series on "Evolutionary Computation in Communications, Networks, and Connected Systems", co-organizer of the European workshop series on "Evolutionary Computation in Transportation and Logistics", and co-chair of the program commitee of the GA track at GECCO 2006. He is conference chair of GECCO 2009.

 


Computational Complexity and Evolutionary Computation:

Description of Tutorial

Evolutionary algorithms and other nature-inspired search heuristics like ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. In recent years, analyses have shown that these general randomized search heuristics can be analyzed like "ordinary" randomized algorithms and that such analyses of the expected optimization time yield deeper insights in the functioning of evolutionary algorithms in the context of approximation and optimization. This is an important research area where a lot of interesting questions are still open.


The tutorial enables attendees to analyze the computational complexity of evolutionary algorithms and other search heuristics in a rigorous way. An overview of the tools and methods developed within the last 15 years is given and practical examples of the application of these analytical methods are presented.



Thomas Jansen

Biosketches:

Thomas Jansen,
born 1969, studied Computer Science at the University of Dortmund, Germany. He received his diploma (1996) and Ph.D. (2000) there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De Jong's EClab at the George Mason University in Fairfax, VA. Since September 2002 he holds a position as Juniorprofessor for Computational Intelligence at the Technical University of Dortmund. He is an associate editor of Evolutionary Computation (MIT Press). His research is centered around design and theoretical analysis of evolutionary algorithms and other randomized search heuristics. He is a programm chair at PPSN 2008 and co-organizes FOGA 2009.




Frank Neumann studied Technical Computer Science in Dortmund and Kiel, Germany. He received his diploma (2002) and Ph.D. (2006) from the Christian-Albrechts-University of Kiel. Since November 2006 he is a Post-Doc in the research group "Algorithms and Complexity" (headed by Kurt Mehlhorn) of the Max-Planck-Institute for Computer Science in Saarbrücken, Germany. A central topic in his work are theoretical aspects of randomized search heuristics in particular for problems from combinatorial optimization. He has been a co-chair of the track "Formal Theory" at GECCO 2007 and chaired the track "Evolutionary Combinatorial Optimization" at GECCO 2008.


Frank Neumann

 


Experimental Research in EC:

Description of Tutorial

It is an open secret that the performance of algorithms depends on their parameterizations --- and of the parameterizations of the problem instances. However, these dependencies can be seen as means for understanding algorithm's behavior. Based on modern statistical techniques we demonstrate how to tune and understand algorithms.

We present a comprehensive, effective and very efficient methodology for the design and experimental analysis of direct search techniques such as evolutionary algorithms, differential evolution, pattern search or even classical deterministic methods such as the Nelder-Mead simplex algorithm. Our approach extends the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems.

Optimization practitioners receive valuable hints for choosing an adequate heuristic for their optimization problems---theoreticians receive guidelines for testing results systematically on real problem instances. Based on several examples from theory and practice we demonstrate how SPO improves the performance of many search heuristics significantly. However, this performance gain is not available for free. Therefore, costs of this tuning process are discussed as well as its limitations and a number of currently unresolved open issues in experimental research on algorithms.



Mike Preuss

Biosketches:

Mike Preuss,
is Research Associate at the Computer Science Department, University of Dortmund, Germany (since 2000), where he also received his Diploma degree in 1998.
His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective niching and the experimental methodology for (non-deterministic) optimization algorithms.
He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games.




Thomas Bartz-Beielstein Dr. Thomas Bartz-Beielstein is a professor for Applied Mathematics at Cologne University of Applied Sciences. He has published more than 50 research papers, conference publications, and several books in the field of Computational Intelligence. His research interests include optimization, simulation, and statistical analysis of complex real-world problems.


Thomas Bartz-Beielstein

 

 


Specialized Techniques and Applications


Evolving Quantum Computer Algorithms:

Description of Tutorial

Computer science will be radically transformed if ongoing efforts to build large-scale quantum computers eventually succeed and if the properties of these computers meet optimistic expectations. Nevertheless, computer scientists still lack a thorough understanding of the power of quantum computing, and it is not always clear how best to utilize the power that is understood. This dilemma exists because quantum algorithms are difficult to grasp and even more difficult to write. Despite large-scale international efforts, only a few important quantum algorithms are documented, leaving many essential questions about the potential of quantum algorithms unanswered.

These unsolved problems are ideal challenges for the application of automatic programming technologies. Genetic programming techniques, in particular, have already produced several new quantum algorithms and it is reasonable to expect further discoveries in the future. These methods will help researchers to discover how additional practical problems can be solved using quantum computers, and they will also help to guide theoretical work on both the power and limits of quantum computing.

This tutorial will provide an introduction to quantum computing and an introduction to the use of evolutionary computation for automatic quantum computer programming. No background in physics or in evolutionary computation will be assumed. While the primary focus of the tutorial will be on general concepts, specific results will also be presented, including human-competitive results produced by genetic programming. Follow-up material is available from the presenter's book, Automatic Quantum Computer Programming: A Genetic Programming Approach, published by Springer and Kluwer Academic Publishers.



Lee Spector

Biosketch:

Lee Spector
is a Professor of Computer Science in the School of Cognitive Science at Hampshire College in Amherst, Massachusetts. 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 a member of the SIGEVO executive committee and he was named a Fellow of the International Society for Genetic and Evolutionary Computation.


He is an active author and editor in the field of genetic programming and he serves on the editorial boards of the journals Genetic Programming and Evolvable Machines and Evolutionary Computation.

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


Accelerating Evolutionary Computation with Graphics Processing Units

Description of Tutorial
Graphics Processing Units (GPUs) have become a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. Modern programming tools allow developers to access the parallelism of the GPU in a flexible and convenient way, hiding many low level details from the user.

In this tutorial we shall provide a gentle introduction of how to put GPUs to good use with Evolutionary Computation.


Wolfgang Banzhaf

Biosketches:

Wolfgang Banzhaf
is a Professor of Computer Science and head of the Department of Computer Science at Memorial University of Newfoundland, St. John's, Canada. He received a diploma in Physics from Ludwig-Maximilians University in Munich, Germany in 1982 and a Ph.D. in Physics from the Department of Physics at the University of Karlsruhe, Germany in 1985. His areas of teaching and research include genetic and evolutionary computation, artificial life, self-organization, and applications of computer science to economics, and the natural and social sciences. He was named a Senior Fellow of the International Society for Genetic and Evolutionary Computation (2003), is a recipient of the EvoStar award for outstanding contributions to this field (2007), a member of the SIGEVO executive committee (since its foundation) and is SIGEVO's current treasurer.

He is an active author in the field of genetic programming, served as the first editor-in-chief of the journal Genetic Programming and Evolvable Machines, and is member of a number of journal editorial and advisory boards.

More info: http://www.cs.mun.ca/~banzhaf/




Simon Harding is a post doctoral research fellow in the Department of Computer Science at Memorial University, Canada. His research interests include genetic programming, unconventional computation and parallel computing on consumer grade hardware. He co-chaired a WCCI2008 track on Computational Intelligence on Consumer Games and Graphics Hardware and is co-chair of a GECCO 2009 special session on this topic.

More info: http://www.cs.mun.ca/~simonh/


Simon Harding

For further info on GPGPGPU Computing, see: http://www.gpgpgpu.com/

 



Generative and Developmental Systems

Description of Tutorial

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 new 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

Biosketch:

Kenneth O. Stanley
is an assistant professor in the School 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) and HyperNEAT algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, and interactive evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, and HyperNEAT. He is the chair of the IEEE Task Force on Computational Intelligence and Video Games, and has chaired the Generative and Developmental Systems track at GECCO for the last three years.

 


(Genetic and) Evolutionary Computer Vision

Description of Tutorial

Computer vision and image processing related disciplines are one of the major fields of applications for Evolutionary Algorithms (EAs).

Over the past few years, EAs have been applied to an ever-increasing number of problems in the area of image and vision computing. The problems range from low-level preprocessing tasks, through segmentation, transformation and feature extraction tasks, to high-level object detection, object tracking and object recognition/classification tasks. EAs have themselves 'evolved' from a basic use as tools for optimizing parameters of some pre-defined algorithms, to a more prominent role as elements of the so-called 'vision chain'.

The tutorial aims at providing attendees with a review of this area, illustrating several practical examples of the different facets of evolutionary computer vision.

A part of the tutorial, in particular, will be dedicated to some very recent applications of genetic programming and particle swarm optimization techniques to computer vision. We will show how such techniques can be effectively applied to image and vision problems and often provide promising results.



Mengjie Zhang

Biosketches:

Mengjie Zhang
received a BE and an ME in 1989 and 1992 from China and a PhD in Computer Science from RMIT University, Melbourne, Australia in 2000. Since 2000, he has been working as a Lecturer then a Senior Lecturer now a Reader/Associate Professor at Victoria University of Wellington, New Zealand. His research is focused on Genetic Programming and Evolutionary Computing, Evolutionary Computer Vision, and Data Mining and Machine learning. He has published over 100 academic papers in refereed international journals and conferences in these areas.


He has been supervising over 20 research students. He has been serving as an associated editor or editorial board member for four international journals and as a reviewer of over ten international journals. He has also been serving as a steering committee member and a program committee member for over 40 international conferences in the areas of Evolutionary Computation and Artificial Intelligence. He has been an organizer of the special session of Evolutionary Computer Vision at IEEE Congress on Evolutionary Computation.

He is a member of ACM, IEEE, IEEE Computer Society, IEEE Computational Intelligence Society, IEEE Systems, Man and Cybernetics Society, and the ACM SIGEVO group. He is also serving as a committee member of the IEEE New Zealand Central Section.

Further Information: http://homepages.mcs.vuw.ac.nz/~mengjie/




Stefano Cagnoni graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student and a post-doc until 1997, working in the Bioengineering Lab of the Department of Electronic Engineering and Telecommunications. He received the PhD degree in Bioengineering in 1993. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997, he has been with the Department of Computer Engineering of the University of Parma, where he is currently Associate Professor.


Stefano Cagnoni

Stefano Cagnoni's main research interests are in the fields of Computer vision, Robotics, Evolutionary Computation and Neural Networks. He is editor-in-chief of the "Journal of Artificial Evolution and Applications", chair of EvoIASP, the European Workshop on applications of Evolutionary Computation to Image Analysis and Signal Processing, and member of the Programme Committee of several International Conferences and Workshops. He has been reviewer for over 15 journals.

Further Information: http://www.ce.unipr.it/people/cagnoni/




Gustavo Olague

Gustavo Olague is a research scientist at the CICESE Research Center working within the Computer Science Department of the Applied Physics Division. He hold a Bachelor's degree (Honors) in Electronics Engineering and a Master's degree in Computer Science from the Instituto Tecnológico de Chihuahua, Mexico. He received the Ph.D. (Diplôme de Doctorat en Imagerie, Vision et Robotique) in Computer Graphics, Vision and Robotics from Institut National Polytechnique de Grenoble, France, working at the INRIA Rhône Alpes within the MOVI Research team. He is presently a Professor of Computer Science at Centro de Investigación Científica y de Educación Superior de Ensenada, B.C.

Olague's research focuses on the principles of computational intelligence applied to close-range photogrammetry and computer vision. He is a member of the EvoNET, RSPSoc, ASPRS, SIGEVO, IEEE, IEEE Computer Society, and is listed in Who's Who in Science and Engineering. Dr. Olague is recipient of the "2003 First Honorable Mention for the Talbert Abrams Award", offered by the American Society for Photogrammetry and Remote Sensing. He has been awarded the Bronze Medal at the Human Competitive Awards for his work on synthesis of interest points in 2006.

Further information: http://cienciascomp.cicese.mx/evovision/



Large scale data mining using Genetics-Based Machine Learning:

Description of Tutorial

We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. 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 parallelization degrees. 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 answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.



Jaume Bacardit

Biosketches:

Dr. Jaume Bacardit received his Ph.D. in 2004 from the Ramon Llull University in Barcelona, Spain. His thesis studied the adaptation of the Pittsburgh approach of Learning Classifier Systems (LCS) to Data Mining tasks. In 2005 he joined the University of Nottingham, UK as a postdoctoral researcher to work on the application of LCS to data mine large-scale bioinformatics datasets and extract interpretable explanations from the learning process. In 2008 he was appointed as a Lecturer in Bioinformatics at the University of Nottingham. This is a joint post between the schools of Biosciences and Computer Science with the aim of developing interdisciplinary research at the interface of both disciplines.


In the School of Computer Science he is part of the ASAP research group. In the School of Biosciences he is part of the Multidisciplinary Centre for Integrative Biology (MyCIB). His research interests include the application of Learning Classifier Systems and other kinds of Evolutionary Learning to data mine large-scale challenging datasets and, in a general sense, the use of data mining and knowledge discovery for biological domains.



Dr. Xavier Llorá interests and work on genetics-based machine learning (GBML) have earned him a spot among the leaders of the renaissance of learning classifier systems (LCSs). His 2002 PhD dissertation challenged traditional data-mining techniques by showing the effectiveness of GBML approaches. He has help organize two edition of the International Workshop of Learning Classifier Systems books and their proceedings. He served as LCS/GBML track chair in the ACM SIGEVO organized GECCO 2005 conference and also organized the NCSA/IlliGAL Gathereing on Evolutionary Learning in 2006. Llorá, awarded with a young research fellowship by the Catalan Government, started his academic career at the Ramon Llull University (Barcelona, Spain) where he earned his PhD degree with honors. In 2003 he moved to the University of Illinois at Urbana-Champaign where he joined the Illinois Genetic Algorithms Laboratory.


Xavier Llorá


Since then, Llorá has headed the DISCUS project (http://www-discus.ge.uiuc.edu/), a project to support collaborative human-innovation and creativity. In summer 2005, Llorá was named research assistant professor at the University of Illinois at Urbana-Champaign and in 2006 a NCSA affiliate where he pursues the development of data-intensive computing infrastructure for large-scale data and text mining under the SEASR project (http://seasr/org).


Evolutionary Multiobjective Combinatorial Optimization (EMCO):

Description of Tutorial

Discrete/combinatorial optimization has been an interesting and challenging area to researchers belonging to development of algorithms and OR techniques for real-world applications. Most such problems abstracted/taken from graph theory are NP-complete in single objective, and NP-hard for multiple objectives. There are many applications of such problems in communication topology and VLSI design.

For most such problems, there do not exist good heuristics/algorithms, therefore, black-box optimization techniques, e.g., EAs are usually preferred for getting effective solutions. In this tutorial, we will do a quick review of some of the standard combinatorial optimization problems, and then include the techniques and design of operators to effectively solve EMCO problems taken from real-world applications. This would be followed by some case-studies taken from standard problems. This interdisciplinary tutorial provides a practical foundation for:

• an introduction to combinatorial problems and their multiobjective versions,
• problem challenges and solution through EMO techniques,
• need of designing specialized genetic operators, representation and techniques for embedding of (limited) problem specific knowledge,
• few case studies taken form well known EMCO, and comparison of EMO results with the existing heuristics/approximation algorithms, and
•improvement of the EMO solutions through hybridization using local search and others.

Key design considerations to designing operators/representations so as to effectively explore the search space will be looked into. In general, the tutorial is aimed at quickly laying a foundation that can be used as the basis for further study, research and development in this emerging area.


Rajeev Kumar

Biosketch:

Rajeev Kumar is a Professor of Computer Science & Engineering (CSE) at Indian Institute of Technology (IIT), Kharagpur. He received his Ph.D. from University of Sheffield, and M.Tech. from University of Roorkee (now, IIT - Roorkee) both in Computer Science & Engineering. He had been a faculty of CSE at IIT Kanpur and BITS Pilani. He had worked for National Semiconductors, Germany and DRDO, India. His main research interests include evolutionary algorithms & multiobjective combinatorial optimization, embedded & multimedia systems, and programming language & software engineering.

He has published over 150 research articles in lead journals and conferences. He is a senior member of ACM, senior member of IEEE and a fellow of IETE. More info about him can be found at http://www.facweb.iitkgp.ernet.in/~rkumar/.



Experimental Optimization by Evolutionary Algorithms:

Description of Tutorial

This tutorial addresses applications of evolutionary algorithms to optimization tasks where the function evaluation cannot be done through a computer simulation, but requires the execution of an experiment in the real world (i.e., cosmetics, detergents, wind tunnel experiments, taste experiments, to mention a few). Those applications can, as we show in the tutorial, be handled very successfully by means of evolutionary algorithms.

To start with, the tutorial provides an overview of the landmark experimental optimization carried out by means of evolutionary algorithms, since their foundation in the late 60's. The main characteristics of such experimental work, in comparison to optimization of simulated systems, are discussed and practical guidelines for real-world experiments with EAs are outlined.

Special emphasis will be put on the first experimental work carried out at the Technical University Berlin, as well as on experimental quantum control, an emerging field in physics, with a presentation of state-of-the-art experiments.

In addition, some arguments are given for the usefulness of evolutionary algorithms in comparison to classical approaches (Design of Experiments), and we will discuss the role of noise in experimental optimization.


Ofer Shir

Biosketch:

Ofer Shir, PhD, is currently a Postdoctoral Research Associate at Princeton University, conducting research at the Rabitz Group, within the Department of Chemistry.

Ofer received his B.Sc. in Physics and Computer Science from the Hebrew University of Jerusalem in June 2003.
His graduate studies were in Leiden University, The Netherlands, where he studied Niching in Evolution Strategies under Prof. Thomas Bäck. He also played a role of a Teaching Assistant in the Computer Science Department of Leiden University during the years 2005-2007.

His research activities included a joint project with Amolf-Amsterdam, where he collaborated with the XUV group of Prof. Marc Vrakking. His PhD in Computer Science from Leiden University was conferred in June 2008. His current topics of interest include Natural Computing, Evolutionary Algorithms, Experimental Optimization, Quantum Control and Quantum Information.



Thomas Bäck, PhD, 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.


Thomas Bäck

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

 


Synthetic Biology II: Modeling & Optimization:

Description of Tutorial

The ultimate goal of systems biology is the development of executable in silico models of cells and organisms. Systems biology attempts to provide an integrative methodology, which while able to cope with -on the one hand- the data deluge that is being generated through high throughput experimental technologies -and on the other hand- emerging technologies that produce scarce often noisy data, would allow to capture within human-understandable models and simulations novel biological knowledge.

In its more modest instantiations, Systems Biology seeks to *clarify* current biological understandings by formalizing what the constitutive elements of a biological system are and how they interact with each other and also it seeks to aid in the *testing* of current understandings against experimental data. In its most ambitious incarnations, however, it aims at *predicting* the behavior of biological systems beyond current understanding and available data thus shedding light onto possible new experimental routes that could lead to better theoretical insights.

Synthetic biology, on the other hand, aims to implement, in vitro/vivo, organisms whose behavior is engineered. The field of synthetic biology holds a great promise for the design, construction and development of artificial (i.e. man-made) biological (sub)systems thus offering viable new routes to genetically modified organisms, smart drugs as well as model systems to examine artificial genomes and proteomes. The informed manipulation of such biological (sub)systems could have an enormous positive impact on our societies, with its effects being felt across a range of activities such as the provision of healthcare, environmental protection and remediation, etc. The basic premise of synthetic biology is that methods commonly used to design and construct non-biological systems, such as those employed in the computational sciences and the engineering disciplines, could also be used to model and program novel synthetic biosystems. Synthetic biology thus lies at the interface of a variety of disciplines ranging from biology through chemistry, physics, computer science, mathematics and engineering.

In this tutorial I will provide an entry level understanding to Systems and Synthetic Biology, it goals, methods and limitations. Furthermore I will describe the many potential applications of evolutionary computation to these two fields. Indeed, I believe that the EC community has a beautiful new application domain in which its methods could be both valued and challenged.

More information is available at www.cs.nott.ac.uk/~nxk


Natalio Krasnogor

Biosketch:

Natalio Krasnogor is an Associate Professor in the School of Computer Science at the University of Nottingham. He is a member of the Automated Scheduling, Optimisation and Planning Research Group (ASAP) and also leads the Interdisciplinary Optimisation Laboratory (IOL). Krasnogor's research activities lie at the interface of Computer Science and the Natural Sciences, e.g. Biology, Physics, Chemistry. In particular, he develops innovative and competitive search methodologies and intelligent decision support systems for transdisciplinary optimisation, modelling of complex systems and very-large datasets processing.


He has applied his expertise to Bioinformatics, Systems Biology, Synthetic Biology, Nanoscience and Chemistry. He is member of the editorial boards for the journal Modelling and Simulation in Engineering and the journal Artificial Evolution and Applications. He is associate editor of the Evolutionary Computation journal and founding technical editor-in-chief of the new journal Memetic Computing. Krasnogor has acted as grant reviewer for the EPSRC (UK), BBSRC(UK), European Science Foundation, European Union, The Israel Science Foundation, DOE Computational Biology Programme (USA), CNRS (France), etc.He co-chairs the 2nd European Conference on Synthetic Biology (ECSB II). More details are available at www.cs.nott.ac.uk/~nxk


Synthetic Biology I: Fundamentals & Devices:

Description of Tutorial

The new field of Synthetic Biology has opened the doors to engineers to design, model, build and test cells with particular behaviours, including computational behaviours. The attraction of using cells as devices stem from the size, robustness, reproducibility of cells, as well as the many built-in input and output interfaces that cells have or can be made to have.

This talk is divided to two parts. First comes (Varin) with an introduction to the biological fundamentals necessary for the full appreciation of the second part (given by Kharma). Topics touched upon in the first part include:

* Gene structure and function;
* Gene regulation;
* Recombinant DNA technologies;
* Construction of synthetic gene networks.

The second part provides examples of how biologists and engineers have successful modified the genomes of various cell types to synthesize cellular computing devices and systems. These examples include (but are not limited to):

* Basic and more complex logic circuits;
* Clocks and sequential circuits;
* Extension of functionality via inter-cellular signaling;
* Means of configuring cells to give them different functionalities.

The importance of synthetic biology to evolutionary computation is that it provides engineers with an opportunity to study and be inspired by the intricacies of real life, while forging ahead with their endeavour to emulate it, simulate it, as well as ultimately create it.

Biosketches:

Dr. Luc Varin is an associate professor of Biology at Concordia University. In June 2000, he received the C.D. Nelson award from the Canadian Society of Plant Physiology. This award is given in recognition of outstanding contribution by a young scientist to Plant Physiology in Canada. Dr. Varin was a member of the administrative board of FQRNT from 2001 to 2005, and of the FQRNT Finance Committee from 2004 to 2005. Dr. Varin has strong ties with the biotechnology industry and acted as consultant for the Fonds de Solidarité du Québec to provide scientific evaluations prior to investment. As previous CEO of Florisys Inc., he acquired expertise in the management of R&D programs and in IP protection. The main objective of his research program is to characterize the biological function of sulfotransferases in plant growth, development and adaptation to stress.



Dr. Nawwaf Kharma got his B.Eng. degree in Computer Engineering from The City University, London, receiving the Jarogate Prize for best overall academic performance. He got his PhD in Machine Learning form Imperial College, London. Since joining Concordia University in 2000, he has co-authored and authored a textbook on Character Recognition and a chapter on Evolvable Developmental Systems. He has published more than 25 peer-reviewed articles. He has two Canadian software copyrights. Kharma has received a Human-Competitive award from the Genetic and Evolutionary Computation Conference (GECCO) 2006 for his work on multiple ellipse detection (in microscopic images) using multi-population genetic algorithms. He is now leading a small team developing that work into a software prototype, in a 1-year project funded by NSERC?s Idea to Innovation (I2I) programme. He heads the Computational Intelligence Lab at Concordia University.


Nawwaf Kharma

 


Cartesian Genetic Programming:

Description of Tutorial

Cartesian Genetic Programming is a form of genetic programming. It is increasing in popularity. It was developed by 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. In a number of studies, it has been shown to be comparatively efficient to other GP techniques.
Since then, the classical form of CGP has been enhanced in various ways by to include automatically defined functions. Most recently it has been developed by Julian Miller, Wolfgang Banzhaf and Simon Harding to include self-modification operators. This again has increased its efficiency.

The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.


Julian Miller

Biosketches:

Julian Miller
is a lecturer 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 130 refereed papers on evolutionary computation, genetic programming, evolvable hardware, and computational development. He has been chair or co-chair of twelve conferences or workshops in genetic programming, computational development, evolvable hardware and evolutionary techniques.


Dr. Miller chaired of the Evolvable Hardware tracks at the Genetic and Evolutionary Computation Conference in 2002-2003 and the Genetic Programming track chair in 2008. He was co-chair the Generative and Developmental Systems(GDS) track in 2007 and is track co-chair of GDS in 2009. He is an associate editor of the journals IEEE Transactions on Evolutionary Computation, and Genetic Programming and Evolvable Machines. He is an editorial board member of the journals Evolutionary Computation and Unconventional Computing. He has given 35 invited talks at conferences, universities, research institutions and commercial companies.



Simon Harding is a post doctoral research fellow in the Department of Computer Science at Memorial University, Canada. His research interests include genetic programming, unconventional computation and parallel computing on consumer grade hardware. He co-chaired a WCCI2008 track on Computational Intelligence on Consumer Games and Graphics Hardware and is co-chair of tracks at CEC 2009 and GECCO 2009 on this topic.


Simon Harding

For further info on Cartesian GP see:

http://www.cartesiangp.co.uk/

 


Bio-inspired Telecommunications:

Description of Tutorial

Biological systems show a number of properties, such as self-organization, adaptivity, scalability, robustness, autonomy, locality of interactions, distribution, which are highly desirable to deal with the growing complexity of current and next generation networks. Therefore, in recent years a growing number of effective solutions for problems related to networked systems have been proposed by taking inspiration from the observation of natural systems and processes such as natural selection, insect societies, immune systems, cultural systems, collective behaviors of groups of animals/cells, etc.

The aim of the tutorial is to provide the cutting edge research on Bio/Nature inspired approaches to network-related problems. The tutorial will also focus on protocol engineering in order to introduce different frameworks that have been developed to realize such Bio/Nature inspired protocols inside the network stack of the Linux kernel. The designers of routing protocols can use it to verify their Linux model by comparing important performance values obtained from Linux model with the simulation model. Moreover, we also focus on "Formal modeling of Bio-inspired algorithms". The tutorial will also highlight Bio/Natue inspired solutions to MANETs and Sensor Networks. Finally, we will conclude the tutorial with an emerging domain of "Bio-inspired Security Solutions for Enterprize Security".

We believe that the tutorial will be instrumental in highlighting the potential of Bio/Nature inspired protocols in real world networks. The tutorial is intended for telecommunication managers, protocol developers, network engineers, network software developers and optimization researchers and graduate students who want to work in non-linear real time dynamic problems. For further details of the tutorial refer to the web site

http://www.nexginrc.org/muddassar/tutorial-gecco09.html

The following references would significantly help in understanding the tutorial:

1.http://www.springer.com/computer/artificial/book/978-3-540-85953-6 2.http://www.springer.com/computer/artificial/book/978-3-540-74088-9 [Chapter 4]


Muddassar Farooq

Biosketch:

Muddassar Farooq received his B.E. degree in Avionics Engineering from National University of Sciences and Technology (NUST), Pakistan, in 1996. He completed his M.S. in Computer Science and Engineering from University of New South Wales (UNSW), Australia, in 1999. He completed his D.Sc in Informatics from Technical University of Dortmund, Germany, in 2006. In 2007, he joined the National University of Computer & Emerging Sciences (NUCES), Islamabad, Pakistan, as an associate professor. He is also the Director of Next Generation Intelligent Networks Research Center (nexGIN RC) at NUCES. He is the author of the book “Bee-inspired Protocol Engineering: from Nature to Networks” that is published by Springer in 2008.

He has also has coauthored two book chapters in different books on swarm intelligence. He is on the editorial board of Springer’s Journal of Swarm Intelligence. He is also the workshop chair of European Workshop on Nature-inspired Techniques for Telecommunication and Networked Systems (EvoCOMNET) held with EuroGP. He also serves on the PC of well-known EC conferences like GECCO, CEC, ANTS. He is the guest editor of a special issue of Journal of System Architecture (JSA) on Nature-inspired algorithms and applications. His research interests include agent based routing protocols for fixed and mobile ad hoc networks (MANETs), nature inspired applied systems, natural computing and engineering and nature inspired computer and network security systems i.e. artificial immune systems.


Theory of Randomized Search Heuristics in Combinatorial Optimization :

Description of Tutorial

The rigorous mathematical analysis of randomized search heuristics (RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics contains well-known approaches such as Randomized Local Search (RLS), the Metropolis Algorithm (MA), Simulated Annealing (SA), and Evolutionary Algorithms (EAs) as well as more recent approaches such as Ant Colony Optimization (ACO). Such heuristics are often applied to problems whose structure is not known or if there are not enough resources such as time, money, or knowledge to obtain good specific algorithms. It is widely acknowledged that a solid mathematical foundation for such heuristics is needed.

Most designers of RSHs, however, rather focused on mimicking processes in nature (such as evolution) rather than making the heuristics amenable to a mathematical analysis. This is different to the classical design of (randomized) algorithms which are developed with their theoretical analysis of runtime (and proof of correctness) in mind. Despite these obstacles, research from the last about 15 years has shown how to apply the methods for the probabilistic analysis of randomized algorithms to RSHs. Mostly, the expected runtime of RSHs on selected problems is analzyed. Thereby, we understand why and when RSHs are efficient optimizers and, conversely, when they cannot be efficient.

The tutorial will give an overview on the analysis of RSHs for solving combinatorial optimization problems. Starting from the first toy examples such as the OneMax function, we approach more realistic problems and arrive at analysis of the runtime and approximation quality of RSHs even for NP-hard problems. Our studies treat not only simple RLS algorithms and SA but also more complex population-based EAs and even ACO algorithms. The combinatorial optimization problems that we discuss include the maximum matching problem, the partition problem and, in particular, the minimum spanning tree problem as an example where Simulated Annealing beats the Metropolis algorithm in combinatorial optimization. Important concepts of the analyses will be described as well.


Carsten Witt

Biosketch:

Carsten Witt studied Computer Science at the University of Dortmund, Germany, where he received his diploma and Ph.D. in 2000 and 2004, respectively. Since spring 2009, he is an assistant professor at the Technical University of Denmark in Copenhagen. Carsten's main research interests are the theoretical aspects of randomized search heuristics, in particular evolutionary algorithms and ant colony optimization. More information: http://ls2-www.cs.uni-dortmund.de/~witt



Fitness Landscapes and Graphs: Multimodularity, Ruggedness and Neutrality:

Description of Tutorial

The performances of evolutionary algorithms (genetics algorithms, genetic programming, etc.) or local search algotihms (Simulated annealing, tabu search, etc.) depends on the properties of seach space structure. One concept to analyse the search space is the fitness landscapes in which the problem to optimize and the search algorithm are taken into account. The fitness landscape is a graph where the nodes are the potential solutions. The study of the fitness landscape consists in analysing this graph or a relevant partition of this graph according to the dynamic or search difficulty.

This tutorial will give an overview, after an historical review of concept of fitness landscape, of the different ways to define fitness landscape in the field of evolutionary computation. Following, the two mains geometries (multimodal and neutral landscapes) corresponding to two different partitions of the graph, meets in optimization problems and the dynamics of metaheuristics on these will be given. The relationship between problems difficulty and fitness landscapes measures (autocorrelation, FDC, neutral degree, etc.) or the properties of the local optima networks, studied in recent work, will be deeply analysed. Finally, the tutorial will conclude with a brief survey of open questions and the recent researchs on fitness landscapes.



Sébastien Verel

Biosketch:

Sébastien Verel

  • Born in Lisieux (France) on October 29th, 1976.
  • PhD in Computer Science at the University of Nice Sophia-Antipolis (France), ``Study and exploitation of neutral networks in fitness landscapes for hard optimization'', achieved in December 12th, 2005.
  • Current position: Permanent Researcher since 2006 by computer science departement, university of Nice Sophia-Antipolis (France)


Research Interests

web page : http://www.i3s.unice.fr/~verel
  • Theory of evolutionary computation:
    study and measure of hardness of fitness landscapes specialy neutral fitness landscapes.
    • Proposition of fitness/fitness correlation to study dynamic and hardness (fitness cloud),
    • Proposition and analysis of the local optima networks,
    • Analysis of complex neutral fitness landscapes in GA and GP (majority problem, firing squad problem, etc.),
    • Proposition of metaheuristic for neutral fitness landscapes (scuba search).
  • Complex Systems:
    where some "global" properties of the system comes from a large number of "local" interactions.
    • Study of the majority problem in cellular automata,
    • Design of new GA for majority problem.
  • Cellular Genetic Algorithm:
    evolutionary algorithms where the population is structured by a grid or a graph.
    • Proposition of new selection selection method in Cellular GA,
    • Analysis of diversity and convergence time in Cellular GA.
  • Cognitive sciences:
    design of models in cognitive area with the help of evolutionary computation
    • Proposition of Eyes tracking model
    • Proposition of a model on a combinaison of two cognitive tasks: memorization and information retrieval

 


Fitness Landscapes and Problem Hardness in Genetic Programming


Biosketch:

Leonardo Vanneschi

• Born in Florence (Italy) on October 3rd, 1970.
• Laurea (maitrise) “summa cum laude” in Computer Science at the University of Pisa, achieved on February 23rd, 1996.
• PhD in Computer Science at the University of Lausanne (Switzerland), achieved on September 06th, 2004.
• Current Position: Assistant Professor (Ricercatore) by the Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.) of the Science Faculty of the University of Milano-Bicocca (Italy).



Leonardo Vanneschi

Main scientific contributions:
• Problem Hardness in Genetic Programming (GP). I have proposed some new hardness indicators for GP and other measures to characterize fitness landscapes.
•Parallel and Distributed GP. I have studied how the island GP model enables to maintain a higher diversity in the whole GP system, thus producing better quality solutions for many applications.
• Other Techniques to Improve GP Performance. The techniques that I have studied are: (1) reducing the number of test cases to evaluate fitness by means of statistics, (2) using populations which dynamically adapt their size according to some events happening during the evolution, (3) coevolving GP terminal symbols by means of Genetic Algorithms (GAs).
• Drug Discovery by Genetic Programming: I have realized a GP framework to mine large datasets of molecule aimed at discovering the model explaining the relationship between molecular descriptors and some important drug features, like toxicity, bioavailability or docking energy.
• Traffic Control Applications using Machine Learning. Using image sequences taken by a fixed camera, I have realized a system to recognize, classify and track the vehicles traveling along a street.
Techniques used are Neural Networks, GAs and IPAN Tracker. The system has been embedded with a television camera system built by the Comerson Srl and it is presently in use in some city crossroads and motorways.
• Genetic Algorithms for the Chemical Formulation Problem. This problem is very important to build new and performing chemical compounds and drugs. I have formalized it as an optimization problem and solved it in efficient way by means of Evolutionary Algorithms.

Web page:
http://personal.disco.unimib.it/Vanneschi

 

 
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