Introduction to Evolutionary Computing

by A.E. Eiben, J.E. Smith

The overall structure of this new edition is Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.

Genres: Computer Science, Artificial Intelligence, Nonfiction, Science, Programming, Technology, Textbooks

299 pages, Paperback

First published October 7, 2003

Reviews

[Ergun Coruh · Rating 5 out of 5]

This is a fantastic book (4.5 rating in Amazon), well written, clear. I would like to study evolutionary processes via EC as a hobby project. I'll most likely use Qt/C++ as my development platform and Python for scripting.

[Richard · Rating 3 out of 5]

Useful overview of the field. There’s almost 500 reference, so it feels like a good jumping off point to find more detail on any particular aspect.

[BCS · Rating 5 out of 5]

This book aims to give a thorough introduction to evolutionary computing, covering techniques and methodological issues. It is divided into three sections. The first covers the basics of evolutionary computing, starting with a brief history of the field and ending with a description of popular evolutionary algorithm variants. The second section discusses methodological issues, specifically parameter tuning and performance measures. The final section is a bit of a catch all, entitled ‘Advanced Topics.’

I enjoyed the coverage in this last section on hybrid evolutionary algorithms, where an evolutionary computing approach is combined with heuristic methods.

I feel that the book does a good job of giving a general overview of the field. It assumes very little initial knowledge and the breath of its coverage is very impressive. If anything, this is the book’s weakness as I did find that it was lacking in detail at times. However, the supporting website does contain suggested further reading for each of the chapters.

Review by Barry Wilkes
Originally posted: http://www.bcs.org/content/conWebDoc/...

[Joost]

A solid introduction to the various flavors of evolutionary computing: genetic algorithms, genetic programming, evolutionary strategies, and evolutionary programming. It gives a good overview of the how the various methods work and what their strengths and potential weaknesses are. The book aims to give an overview of the field without going into too much depth. It does contain a large amount of references for more in depth study.

Missing are explanations of methods like particle swarm optimisation, differential evolution, simulated annealing, and ant-swarms, firefly-swarms, etc.

Is a good introduction to the subject and leaves with all the info you needn to start experimenting yourself.

[Victor · Rating 4 out of 5]

Very good introduction to the family of evolutionary computing, I used this book for a course on Evolutionary Computing. Highly recommended, it covers from the very basics to the most new-hot topics.