Item description for Introduction to Evolutionary Computing (Natural Computing Series) by Agoston E. Eiben & J. E. Smith...
Evolutionary Computing is 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, ranging from practical applications in industry and commerce to leading-edge scientific research.
This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. Itis also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied.
Last, but not least, this 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.
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Reviews - What do customers think about Introduction to Evolutionary Computing (Natural Computing Series)?
a comprehensive textbook for freshers in EC Jun 1, 2008
it is a very helpful book for those who want to get the outline of evolutionary computing. it will offer a solid foundation for further study.
good textbook Oct 31, 2006
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 Apr 3, 2006
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 Feb 2, 2005
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 Jun 6, 2004
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