Introduction to Evolutionary Computing

In what is a hugely exciting and fast developing field, here is the first complete overview, covering all algorithm variants. It contains easily accessed reference information on the current state-of-the-art in a wide range of related topics, too.

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

At present the only authored book that contains a complete overview of the field of evolutionary computing, treating all "dialects" and important algorithm variants: GAs, ES, EP, GP, LCS, MAs, MOEAs Provides a single comprehensive source using one conceptual framework and a uniform terminology at a level accessible to undergraduates Includes supplementary material: sn.pub/extras

Autorentext

A.E. Eiben (M.Sc in Maths 1985, Ph.D. in computer science 1991) is one of the European early birds of EC, his first EC paper is dating back to 1989. This was a technical report on Markov chain convergence properties of GAs, that was published in the proceedings of the first European EC conference, the PPSN 1990. Ever since he has been active in the field with special interest in multi-parent recombination, constraint satisfaction, and self-calibrating evolutionary algorithms. During the last decade he was chair or member of the organizing committee of almost all major events of the field: CEC, EP, FOGA, GECCO, PPSN and is a member of the PPSN Steering Committee. Currently he is an editorial board member of premium EC and EC-related jorunals: Evolutionary Computing, Genetic Programming and Evolvable Machines, IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and Natural Computing. Furthermore, he is one of the founders and the executive board members of the European Network of Excellence in Evolutionary Computing, EvoNet. He is one of the series editors of the Springer book series Natural Computing. His also has almost ten years of teaching experience, having given academic and industrial EC courses and organising European EC Summer Schools.

J.E. Smith (Msc. Communicating Computer Systems 1993, PhD in computer science 1998) has been actively researching and publishing on the field of EC since 1994. His work has combined theoretical modelling with empirical studies in a number of areas, especially concerning so-called "self-adaptive" and "hybrid" systems which exhibit the common characteristic of being able to "learn how to learn". This research has been backed up with industrial collaborations applying EC-based (and other) techniques to a range of diverse problems such as VLSI verification and bio-informatics. For a number of years he has served on the programme committees of all of the major (and many smaller) conferences in thefield, and as a reviewer for all of the principal journals. Since 2000 he has been one of the co-organisers of the annual International Workshop on Memetic Algorithms (WOMA). In addition to teaching courses in Evolutionary Computing in academia and industry, he has been a member of the Training Committee of the European Network of Excellence in Evolutionary Computing, EvoNet, since its formation and as such has been heavily involved in the production of a variety of different training materials for the EvoNet "flying circus".

Inhalt
1 Introduction.- 2 What is an Evolutionary Algorithm?.- 3 Genetic Algorithms.- 4 Evolution Strategies.- 5 Evolutionary Programming.- 6 Genetic Programming.- 7 Learning Classifier Systems.- 8 Parameter Control in Evolutionary Algorithms.- 9 Multimodal Problems and Spatial Distribution.- 10 Hybridisation with Other Techniques: Memetic Algorithms.- 11 Theory.- 12 Constraint Handling.- 13 Special Forms of Evolution.- 14 Working with Evolutionary Algorithms.- 15 Summary.- A Gray Coding.- B Test Functions.- References.