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.

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.

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.

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.

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.

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).

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 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.

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 bio-inspired computation, multiobjective optimization, computational biology, and computer engineering applications. Dr. Zitzler was General Co-Chairman of the first three international conferences on evolutionary multi-criterion optimization (EMO 2001, EMO 2003, and EMO 2005).

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.

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.