A special feature of IEEE CEC is the Tutorial programme which enables all delegates
to increase their knowledge of evolutionary computation from leading expects in
the field. Topics are offered at introductory, intermediate and advanced
levels in a range of subjects ranging from the fundamentals to specialist
subjects. Tutorials last 2 hours and will take place on May 18 2009, the day
before the main conference begins. Attendance at Tutorial sessions is included
in the conference registration fee and all tutorial slides will be available on
CD as part of the conference registration pack.
Kalyanmoy Deb, Indian Institute of Technology Kanpur
Cartesian Genetic Programming
Julian Miller, University of York, UK
Cartesian Genetic Programming is a form of genetic programming. 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. It employs a representation in which some genes are
non-coding (i.e. are 'junk'). Simple evolutionary algorithms can be used to
exploit this redundancy through genetic drift. In a number of studies, CGP has
been shown to be highly efficient compared to other GP techniques on a range of
benchmark problems.
Since its original inception CGP has been enhanced in various ways by Julian
Miller and James Walker to include automatically defined functions. The
tutorial will cover the classic technique, advanced developments and
applications to a variety of problem domains.
Julian. F. Miller, BSc (Lond), PhD (City), PGCLTHE (Bham). Dr. Miller's
obtained his first degree in Physics from the University of London in 1980 and
obtained his doctorate in Nonlinear Mathematics from the City University in
1988. He worked at Napier University from 1989-1999 and the Natural Computation
research group in the School of Computer Science at the University of
Birmingham from 1999-2003. He joined the Department of Electronics at the
University of York in Oct 2003. He has chaired twelve international workshops
and conferences in Evolutionary Computation, Genetic Programming (GP) and
Evolvable Hardware. He is an associate editor of the Journal of Genetic
Programming and Evolvable Machines and the IEEE Transactions on Evolutionary
Computation. He is on the editorial board of the journals: Evolutionary
Computation and International Journal of Unconventional Computing. His
research is concerned with, but are not limited to: the enhancement of
Cartesian Genetic Programming (CGP), applications of CGP, and models and
applications of computational development. Dr Miller a highly cited author with
over 2000 citations and over 140 publications.
Co-evolutionary Learning of Game-playing Strategies
Xin Yao, CERCIA, University of Birmingham, UK
Co-evolutionary learning has been used to learn strategies for playing various
games, from iterated prisoner's dilemma to chess and from trading games to
checkers. This tutorial uses iterated prisoner's dilemma games as an example to
illustrate how co-evolution can be used to learn game-playing strategies
without any teacher information. A number of important research issues, which
are not specific to any particular games, will be discussed, including:
Representation of strategies and the genotype-phenotype mapping
The role of diversity (including genetic diversity and behavioural diversity) in co-evolutionary learning
Generalisation ability of evolved strategies and an ensemble approach to improve generalisation ability
A vigorous quantitative framework for measuring generalisation of co-evolutionary learning
Selected References (downloadable from my web site):
X. Yao and P. Darwen, An experimental study of N-person iterated prisoner's dilemma games, Informatica, 18(4):435--450, 1994.
P. J. Darwen and X. Yao, Speciation as automatic categorical modularization, IEEE Transactions on Evolutionary Computation, 1(2):101-108, 1997.
X. Yao and P. J. Darwen, How Important Is Your Reputation in a Multi-Agent Environment, Proc. of the 1999 IEEE Conference on Systems, Man, and Cybernetics, IEEE Press, Piscataway, NJ, USA, pp.II-575 - II-580, October 1999.
S. Y. Chong and X. Yao, Behavioral Diversity, Choices, and Noise in the Iterated Prisoner's Dilemma, IEEE Transactions on Evolutionary Computation, 9(6):540-551, December 2005.
S. Y. Chong and X. Yao, Multiple Choices and Reputation in Multi-Agent Interactions, IEEE Transactions on Evolutionary Computation, 11(6):689-711, December 2007.
S. Y. Chong, P. Tino and X. Yao, Measuring Generalization Performance in Co-evolutionary Learning, IEEE Transactions on Evolutionary Computation, 12(4):479-505, August 2008.
Evolutionary Computation: A Unified Approach
Kenneth De Jong, Department of Computer Science, George Mason University
The field of Evolutionary Computation has experienced tremendous growth over
the past 20 years, resulting in a wide variety of evolutionary algorithms and
applications. The result poses an interesting dilemma for many practitioners
in the sense that, with such a wide variety of algorithms and approaches, it is
often hard to 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 a general EC framework that
can help compare and contrast approaches, encourages crossbreeding, and
facilitates intelligent design choices. The use of this framework is then
illustrated by showing how traditional EAs can be compared and contrasted with
it, and how new EAs can be effectively designed using it.
Finally, the framework is used to identify some important open issues that need
further research.
Evolutionary Computation in Finance and Economics
Edward Tsang, University of Essex, UK
Computing has changed many aspects of our daily life. It certainly has shaken
the foundation of finance and economics research and changed the research
frontier. Advances in both hardware and software allow us to study finance and
economic in ways that were previously impossible. Expertise in both computing
and finance and economics allow us to make impacts that neither computer
scientists nor financial experts or economists can make on their own. For
example, evolutionary computation has helped us to discover forecasting rules,
bargaining strategies and economic models. The use of artificial markets
enables us to understand better some of the fundamental concepts in economics,
such as rationality and the efficient market hypothesis. Finance and economics
are not the only disciplines that benefit from this interdisciplinary research.
Computer science benefit too, as applications drive research. Search techniques
were developed to focus an evolutionary in solutions that are important to
users. The need for finding scarce opportunities motivated new computational
methods in genetic programming. In this tutorial, I shall give an overview of
research in these techniques as well as their applications.
Edward Tsang is professor of Computer Science at the University of Essex. He
specializes in business application of artificial intelligence and his research
interests include artificial intelligence applications, computational finance,
constraint satisfaction, evolutionary computation, and heuristic search. He was
co-founder and Deputy Director (2003-2008) of Centre for Computational Finance
and Economic Agents (CCFEA) at University of Essex and is also a founding
member and Deputy Director (2008-) of the Centre for Computational Intelligence
at University of Essex, which specializes in applications of computational
intelligence techniques. Edward is the author of Foundations of Constraint
Satisfaction, the first book to define the scope of the field and founded the
Computation Finance and Economics Technical Committee in IEEE's Computational
Intelligence Society in 2004. He has been consultant to GEC Marconi, British
Telecom, the Commonwealth Secretariat and other organizations.
Evolvable Hardware
Jim Tørresen, University of Oslo
Lukas Sekanina, Brno University of Technology
Traditionally, hardware has been static at run-time. However, with the
introduction of reconfigurable technology and devices, dynamic hardware is now
possible to realize by using automatic design schemes. One method for automatic
design is evolvable hardware. The recent years of development of this field can
be characterized as a continuous search for promising problems from the point
of view of evolutionary design.
In the first part of the tutorial, fundamental concepts of evolutionary circuit
design and evolvable hardware will be introduced. Examples of evolved
innovative designs will be presented in domains of small combinational
circuits, middle-size circuits (such as image filters or arithmetic circuits)
and large circuits (benchmarks for testability analysis methods), covering thus
circuit complexity from a few gates to millions of gates. The second part will
give an overview of the field targeting run-time evolvable systems. This
includes an introduction of reconfigurable technology followed by a description
of a number of the adaptable architectures that have been proposed. Challenges
related to evolving systems in operation will also be addressed. A special
focus will be given to the architectures designed for real-world applications.
Jim Torresen received his M.Sc. and Dr.ing. (Ph.D) degrees in computer
architecture and design from the Norwegian University of Science and
Technology, University of Trondheim in 1991 and 1996, respectively. He has been
employed as a senior hardware designer at NERA Telecommunications (1996-1998)
and at Navia Aviation (1998-1999). Since 1999, he has been a professor at the
Department of Informatics at the University of Oslo (associate professor
1999-2005). Jim Torresen has been a visiting researcher at Kyoto University,
Japan for one year (1993-1994) and four months at Electrotechnical laboratory,
Tsukuba, Japan (1997 and 2000).
His research interests at the moment include reconfigurable hardware,
evolvable hardware, system-on-chip design and applying this to complex
real-world applications. Several novel methods have been proposed. He has
published a number of scientific papers in international journals, books and
conference proceedings. He is in the program committee of more than ten
different international conferences as well as a regular reviewer of a number
of international journals (mainly published by IEEE and IET). He also acts as
an evaluator for proposals in EU FP7.
Lukas Sekanina (MSc - 1999, PhD - 2002) received all his degrees from Brno
University of Technology, Czech Republic. He was awarded the Fulbright
scholarship and worked on the evolutionary circuit design with NASA Jet
Propulsion Laboratory in Pasadena in 2004. He was a visiting lecturer with
Pennsylvania State University and visiting researcher with Department of
Informatics, University of Oslo in 2001. Lukas Sekanina is author of the
monograph Evolvable Components (Springer Verlag, 2004). He co-authored more
than 60 papers mainly on evolvable hardware. He has served as a program
committee member of several conferences and received several awards for his
work (including the Silver Medal at Humies 2008). Currently he is an associate
professor and deputy head of the Department of Computers Systems at the Faculty
of Information Technology, Brno University of Technology. Member of IEEE. For
more information, see http://www.fit.vutbr.cz/~sekanina.
Genetic Programming Practice and Theory
Riccardo Poli, Department of Computing and Electonic Systems, University of Essex, UK
Genetic programming (GP) is an evolutionary technique for getting
computers to automatically solve problems without having to tell them
explicitly how to do it (Koza, 1992). Since its inception genetic
programming has been used to solve many practical problems including
producing a number of human competitive results and even patentable
new inventions (Poli et al, 2008).
This tutorial includes two parts. In the first part I will introduce
the basics of GP practice, briefly explaining GP representations,
operators and search algorithm, and showing examples of real
runs. This will mainly be based on (Koza, 1992) and (Poli et al,
2008). In the second part of the tutorial, I will then concentrate on
explaining how and why GP works. This will done by first
characterising GP's search space (the space of all possible programs)
and then by explaining the way in which GP explores such a space. This
will be based mainly on (Langdon and Poli, 2002) and (Poli et al,
2008).
Despite its technical contents, the tutorial will require limited
mathematical background.
J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
W.B. Langdon and R. Poli, Foundations of Genetic Programming, Springer, 2002.
R. Poli, W.B. Langdon and N.F. McPhee, A Field Guide to Genetic Programming, Lulu.com, 2008 (freely available from the Internet).
Hyper-heuristics: An Emerging Paradigm in Evolutionary Computation
Edmund Burke, School of Computer Science, University of Nottingham, UK
This tutorial will introduce and discuss an emerging methodology in
search/optimisation and decision support systems: Hyper-heuristics. The term
can be thought of as describing "heuristics to choose heuristics". It is
concerned with search methods which explore a search space of heuristics
(rather than a search space of potential solutions to a problem). The approach
is (partially) motivated by the aim of investigating the automation of the
heuristic design process. The hope is that hyper-heuristics will lead to more
general systems that are able to automatically operate over a wider range of
problem domains than is possible today. The goal is to develop methodlogies
which can adapt to different environments without manually having to customise
the search, or its parameters, for each particular problem domain. This can be
seen as one of the drawbacks of many current metaheuristic and evolutionary
implementations, which tend to have to be customised for a particular class of
problems or even specific problem instances. Of course, evolutionary methods
and other metaheuristics can be developed and employed as hyper-heuristics.
Indeed, recently published work has explored a range of metaheuristic and
evolutionary algorithms within the context of hyper-heuristics and this
tutorial will present and discuss these (and other) approaches.
Although the term hyper-heuristic is relatively new, the basic idea has
actually been around for over 40 years and this tutorial will give a
brief history of the area, before discussing work carried out to date
and then focussing on some observations and promising research directions.
Introduction to the use of EAs in Bioinformatics
L. Gwenn Volkert, Kent State University
Clare Bates Congdon, Colby College
Daniel Ashlock, University of Guelph
This tutorial is aimed at researchers interested in learning about the field
of bioinformatics and some of the biological background needed to undertake EA
based work in this area. It presumes no particular background in biology and is
intended to equip newcomers with the necessary background to participate in the
bioinformatics related sessions and presentations at CEC 2009 and help new
researchers in developing research projects in this growing area. Topics
covered will include an overview of biological basics, including the "central
dogma" of biology, gene and protein structure, biotechnologies and
bio-databases, followed by an overview of several problems in bioinformatics
that are particularity well suited to evolutionary algorithm based approaches,
including a look at several hybrid approaches.
L. Gwenn Volkert received a PhD in Computer Science from Wayne
State University in 2001. She is an Associate Professor of Computer
Science at Kent State University where she is also a faculty member in
the School of Biomedical Science. Dr. Volkert is a senior member of
the IEEE and is currently the chair of the IEEE CIS Bioinformatics and
Bioengineering Task Force.
Clare Bates Congdon received a PhD in Computer Science and
Engineering from The University of Michigan in 1995. She is a faculty
member of the Computer Science Department at the University of
Southern Maine where she also serves as Director of Bioinformatics and
Intelligent Systems. She is funded by the NIH INBRE program for her
project "Machine Learning for Phylogenetics and Genomics". Dr.
Congdon is a senior member of the IEEE and a co-chair of the CEC
2009 Special Session on Bioinformatics.
Daniel Ashlock received a PhD in Mathematics from Caltech in 1990.
He is now a Professor of Mathematics and Statistics at the University
of Guelph where he holds a Chair in Bioinformatics. He is currently
funded by the NSF to work on the sequencing of the maize genome
and on the analysis and display of biological networks. He also is
funded by the NIH to work on recombination in retroviruses. Dr.
Ashlock is a senior member of the IEEE and an associate editor of the
IEEE Transactions on Evolutionary Computation.
Learning to Play Games
Simon Lucas, University of Essex, UK
This tutorial provides a practical introduction to game learning with
function approximation architectures. The tutorial will cover the two main
approaches to learning in games: evolution (including co-evolution), and
temporal difference learning (TDL).
It will be shown that the choice of function approximation architecture has
a critical impact on what is learned. In addition to standard MLPs, attention
is also given to N-Tuple systems and interpolated table-based approximators, as
these have recently shown great potential to learn quickly and effectively.
Examples will be used to illustrate how the function approximator must be
appropriately matched with the learning algorithm in order to get best
results.
Each method will be demonstrated with reference to some simple fragments of
software, illustrating how the learning algorithm is connected with the game
and with the function approximation architecture. Example games will include
GridWorld, Othello, and Simulated Car Racing.
Recent results on the information rate limits attainable for these learning
algorithms (in terms of bits of information gained per game played) will also
be discussed.
Dr. Simon M. Lucas (SMIEEE) is a reader in computer science at the University
of Essex (UK). His main research interests are evolutionary computation,
games, and pattern recognition, and he has published widely in these fields
with over 130 peer-reviewed papers, mostly in leading international conferences
and journals. He was chair of IAPR Technical Committee 5 on Benchmarking and
Software (2002 - 2006) and is the inventor of the scanning n-tuple classifier,
a fast and accurate OCR method. He was appointed inaugural chair of the IEEE
CIS Games Technical Committee in July 2006, has been competitions chair for
many international conferences, and co-chaired the first IEEE Symposium on
Computational Intelligence and Games in 2005. He was program chair for IEEE CEC
2006, program co-chair for IEEE CIG 2007, and for PPSN 2008. He is an
associated editor of IEEE Transactions on Evolutionary Computation, and the
Journal of Memetic Computing. He was an invited keynote speaker at IEEE CEC
2007, and an invited tutorial speaker at IEEE WCCI 2008, at PPSN 2008, and at
IEEE CIG 2008. Dr. Lucas was recently appointed as the founding
Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI
in Games.
Particle Swarm Optimization: A Universal Optimizer?
Andries P. Engelbrecht, University of Pretoria, South Africa
The main objective of this tutorial will be to answer the question if
particle swarm optimization (PSO) can be considered as a universal optimizer.
In the context of this tutorial, this means that the PSO can be applied to a
wide range of optimization problem types as well as search domain types. The
tutorial will start with an overview of the original, basic PSO, its origins,
and some of the first adaptations of the basic PSO to improve its performance.
This will be followed by a short discussion on heuristics to select proper
values for control parameters. The remainder and bulk of the tutorial will
cover a classification of different problem types, and will show how PSO can be
applied to solve problems of these types. This part of the tutorial will be
organized in the following sections, one for each problem type:
Constrained versus unconstrained problems, also covering boundary constraints
Multi-objective optimization
Dynamic environments
It will be shown that PSO can solve these problems with minor adaptation of the
basic PSO without violating the foundational principles of PSO. For each of these
problem types a small subset of the most successful algorithms will be discussed.
Principled Approaches to tuning EA parameters
A.E. Eiben, Vrije Universiteit Amsterdam
Finding appropriate parameter values for evolutionary algorithms (EA) is one
of the persisting grand challenges of the evolutionary computing (EC) field.
On the one hand, all EC researchers and practicioners acknowledge that good
parameter values are essential for good EA performance. On the other hand,
even after 30 years of EC research there is very little known about the effect
of EA parameters on EA performance. Users mostly rely on conventions (mutation
rate should be low), ad hoc choices (why not use uniform crossover), and
experimental comparisons on a limited scale (testing combinations of three
different crossover rates and three different mutation rates). Hence, there is
a striking gap between the widely acknowledged importance of good parameter
values and the widely exhibited ignorance concerning principled approaches to
tune EA parameters.
The aims of this tutorial are threefold: creating awareness, providing
guidelines, and presenting a vision for future development. As for the
awareness, we will discuss the matter of EA parameters, catagorize different
ways of setting them, and discuss the most important related issues, inlcuding
performance measures and test functions. In the guidelines section we review
existing algorithmic approaches to parameter tuning. We will discuss sweep
methods, search methods, and combined methods, positioning them on a feature
map and present (comparative) results on their usefulness. In the last part,
we take a future-oriented attitude and identify research areas with high
relevance and potential impact.
Recent Challenges to Evolutionary Multi-Criterion Optimization (EMO)
Kalyanmoy Deb, Indian Institute of Technology Kanpur, India
Many real-world search and optimization problems involve multiple objectives
which are conflicting to each other. These problems give rise to a set of
Pareto-optimal solutions which must be found and a decision-making task must be
used to choose a particular preferred solution. Since 1993, Evolutionary
algorithms (EAs) have been amply demonstrated to find a well-distributed set of
near Pareto-optimal solutions in many problems. In this tutorial, we shall
discuss a number of such evolutionary multi-objective optimization (EMO)
techniques, assess the current state-of-the-art techniques, and highlight a
number of recent challenges which must be paid more attention. Some of these
challenges include:
Inclusion of multi-criterion decision-making aides within EMO framework so as to achieve both optimization and decision-making tasks to find a single preferred solution.
Handling a large number of objectives (such as 10 or more) which often arise in real-world scenarios.
Handling practical complexities using EMO, such as uncertainties in decision variables and parameters, reliability issues, computationally demanding solution evaluation, non-linear constraints etc.
Handling dynamic multi-objective optimization problems in which objectives and constraints change with the progress of an EMO simulation
Use of EMO principles to other problem solving tasks
Use of EMO methodology for knowledge extraction in real-world problems
This tutorial 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.
