Introduction to Evolutionary Computing

by A.E. Eiben and J.E. Smith

Book Overview

Problems to Be Solved.- Evolutionary Computing: The Origins.- What Is an Evolutionary Algorithm?.- Representation, Mutation, and Recombination.- Fitness, Selection, and Population Management.- Popular Evolutionary Algorithm Variants.- Hybridisation with Other Techniques: Memetic Algorithms.- Nonstationary and Noisy Function Optimisation.- Multiobjective Evolutionary Algorithms.- Constraint Handling.- Interactive Evolutionary Algorithms.- Coevolutionary...

• Format: Paperback
• Language: English
• ISBN: 3662499851
• ISBN13: 9783662499856
• Release Date: October 2016
• Publisher: Springer
• Length: 287 Pages
• Weight: 0.94 lbs.
• Dimensions: 0.6" x 6.1" x 9.2"

Customer Reviews

There's a reason my Prof raves about the book

My CS professor has used this book the last three times he has taught this class. Considering the fact that he is helping to host the Eighth GECCO Undergraduate Student Workshop as part of the 2010 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2010), it means that he knows a little bit about the subject. He is actually the contact point for the Student Workshop. The page for the Workshop is here: [...] The book itself is to the point. It is a little brief, but the diagrams help and the subject matter is covered well enough that you can simply learn the basics by reading the book.

good textbook

I have used evolutionary programming in my research in the past and have read several books on the topic. This is one of the most well written books available, that can easily be read by a beginner despite its depth. The conclusions that they draw are logical and supported by the appropriate references (I was not impressed with the theory and results in the field, but this has nothing to do with the quality of this book).

Evolution as a practical tool

The authors emphasise from the get-go that this book is meant as a practical introduction to the application of evolutionary computing. It is not a high brow, abstruse monograph. (Which indeed Springer texts often are.) 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.

Excellent introduction

I taught our introduction to evolutionary computation class from this book. It is a well rounded introduction to the topic covering most of the introductorty material you would expect. There is an real dearth of good introductory books for EC. This is probably the best because of its breadth. Its weakness is its lack of detail. It would not hurt if they covered the same material in about 50% more pages. As soon as they start a topic its over and on to the next topic. But if you are new to the field they give plenty of references and touch on most topics in enough detail for students to implement. All in all a good solid job.

An excellent textbook suitable for all levels

This is an excellent textbook which covers most aspects of the Evolutionary Computing. It's suitable for all levels. It's easy to follow, rich in content and has many references (439 to be precise) for further information. The table of contents from the book's web site is as follows: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. Multi-Modal 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 16. Appendices 17. Index 18. References Recommended to everyone interested in EC.