Genetic Algorithms in Search, Optimization, and Machine Learning | David E. Goldberg

Genetic Algorithms in Search, Optimization, and Machine Learning | David E. Goldberg | Great start to your journey in Genetic Algorithms.

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Genetic Algorithms...

	Genetic Algorithms in Search, Optimization, and Machine Learning David E. Goldberg Addison-Wesley Professional, 1989 - 432 pages average customer review:based on 19 reviews view larger image
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highly recommended

David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.

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Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg

Excellent book for Graduate students and instructors. Highly recommend!

Great start to your journey in Genetic Algorithms.

This is a great book to begin your journey on Genetic Algorithms (GA). The author is a pioneering authority on the subject and has explained the basics of a GA in a very gentle and easy to understand manner. The book has a great variety of specific but diverse examples, which may not be useful at first glance, but gives an insight to where all the technique has been applied!

However, some aspects of the book perhaps need an edition, like the more recent advances in GA operators, specifics of chromosomal representation schemes, non-linear optimization functions, etc. I have read several, well written books on the subject, but this one has a very distinct and sometimes interesting style of writing! The best would be to quickly read this one to get a fairly good understanding of the basics and then take up a recent book that addresses other aspects like Mitchell's book, for example.

Having said that, I think the book is a great and inspiring start to using genetic algorithms.

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Not the only paradigm for evolutionary computation

This book gives a good introduction to genetic algorithms for a general undergraduate audience. However, it is important to note that it does not cover Evolutionary Strategies, an approach to evolutionary computing that I have found quite useful since it is specifically designed for Euclidean space optimization problems where many if not most interesting optimization problems are formulated in (take for example the problem of determining the weights of a neural network that minimizes the network's overall classification error). Nor does it cover evolutionary programming (not to be confused with genetic programming). So after reading this book, I recommend (for the mathematically adventurous) Thomas Back's "Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms"
ISBN: 0195099710

Happy reading and enjoy the fascinating world of evolutionary computation!

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Needs updating

OK, I agree with the previous reviewers: it's the classical textbook for GAs. But it definitely needs updating, as it's a 15-year old book and much has been done in the area. Niching methods, for example, are just outlined. I'd recommend Melanie Mitchell's book instead of this one.

reviews: page 1, 2, 3, 4

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