Genetic Algorithms Books

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There are so many different books on Genetic Algorithms: Amazon's listing has over 621 books! As you might expect, a lot of these aren't especially good. However, a few stand out from the rest. They are presented here...

The cream of the cream, in no particular order:

	Handbook of Evolutionary Computation Thomas Back, David B. Fogel, Zbigniew Michalewicz
	This is an impressive collection of chapters, written by prominent researchers from all the various fields of evolutionary computation: genetic algorithms, evolution strategies, genetic programming. It aims to promote cross-fertilization between these paradigms. It is already considered the standard resource for the field: pretty much everything is covered! Despite its price, this is undoutably the accademic reference for the future.
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	Genetic Algorithms and Engineering Design Mitsuo Gen, Runwei Cheng
	This book is well organised, intuitive and up-to date. It tackles the fundamentals of genetic algorithms, especially in solving constrained and combinatorial optimization problems. This nicely leads into the industrial engineering examples, such as scheduling and production plans, sequencing, transportation and vehicle routing, facility layout, and resource allocation. A very practical book.
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	An Introduction to Genetic Algorithms Melanie Mitchell
	This highly-acclaimed book is considered to be the best introductory text around. Many sware by it, including academic lecturers. It covers the background of genetic algorithms, their history and motivation. Informative examples are given, and discusses their use in scientific models. Finally, a few key areas of future research are described for those aspiring computer scientists among you! Best of all, the book is succinct and elegantly written, accessible to all.
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	Genetic Algorithms in Search, Optimization and Machine Learning David E. Goldberg
	Goldberg is one of the preeminent researchers in the field, working along side the father of genetic algorithms, John Holland. He has published over a hundred papers, and his deep understanding of the subject shines through. This is a very well written book, which starts by providing the theoretical background of GA. A mathematical foundation shows why this optimisation technique works. The rest of the book takes the shape of a tutorial, providing examples and coding exercices. Pascal source code is provided, and although this is a fairly outdated language, this provides good understanding of an implementation. This is one of the best-sellers of the field, and understandably so.
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	Practical Genetic Algorithms Randy L. Haupt, Sue Ellen Haupt
	This book approaches genetic algorithms from a more practical point of view. The fundamental theory is left aside in favour of a clear insight to the field. The concepts of optimisation are first discussed, proceeding into binary genetics. The code is then described, and interesting applications are given. Despite their being no code present, this book does give one a practical feel for genetic algorithms.
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