Introduction to Evolutionary Computing (Natural Computing Series) Hardcover – October 7, 2008
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From the reviews:
"This is intended primarily as a textbook for lecturers and graduate and undergraduate students but will certainly attract a wider readership. The authors explain that each of them has many years of teaching experience, and has given instruction on Evolutionary Computing (EC) … and they realised the need for a suitable textbook and decided to write this one. … Beside serving as an introduction the book is a guide to the state-of-the art. … This is a well-produced and very useful book." (Alex M. Andrew, Robotica, Vol. 22, 2004)
About the Author
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 the field, 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".
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This book gave me exactly that.
I recommend it to all that have to make decisions about using specific forms of evolutionary computing to achieve a goal.
The level of discussion can be adequately understood by someone with a good background in computing and hopefully also in some science or engineering field. Certainly, there are important abstractions that must be mastered. Like how the evolutionary search can be seen as a path across a fitness landscape or potential energy surface. But there appears to be a careful explanation of the minimum necessary maths to convey an idea. And where a chapter's references might point to more specialised texts or journal papers that give a fuller math treatment.
It may well be, as another reviewer remarked, that there is insufficient detail in some passages of this book. But perhaps the text is not meant to be a low level "user's manual" type of discussion.
If you do find this book useful, consider a more advanced text, "Foundations of Genetic Programming" by Langdon and Poli, also published by Springer. It takes you deeper into the subject.
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. Multi-Modal Problems and Spatial Distribution
10. Hybridisation with Other Techniques: Memetic Algorithms
12. Constraint Handling
13. Special Forms of Evolution
14. Working with Evolutionary Algorithms
Recommended to everyone interested in EC.