Item description for Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz...
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
Outline Zbigniew Michalewicz's Genetic Algorithms + Data Structures = Evolution Programs has three sections. The first section is a straightforward introduction to genetic algorithms. In the second section, Michalewicz describes how to apply genetic algorithms to numerical optimization. Michalewicz, who is a pioneer in this field, discusses the rationale for using genetic algorithms for numerical optimization and describes several experiments that show how this new type of genetic algorithm performs. The author devotes the third section of the book to evolution programs.
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Est. Packaging Dimensions: Length: 9.37" Width: 6.38" Height: 1.1" Weight: 1.63 lbs.
Release Date Nov 26, 1998
ISBN 3540606769 ISBN13 9783540606765
Availability 90 units. Availability accurate as of May 29, 2017 05:36.
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More About Zbigniew Michalewicz
Michalewicz, University of North Carolina at Charlotte
Zbigniew Michalewicz was born in 1946 and has an academic affiliation as follows - University of North Carolina.
Reviews - What do customers think about Genetic Algorithms + Data Structures = Evolution Programs?
Lot of ideas for Genetic and Evolutionary Algorithm Jan 5, 2007
This book is not written to be the primary text book for a Genetic Algotithm, Data Structure or a Neural Algorithm course. However this book gives an excellent introduction to modern approaches to Evolutionary Algorithm, and how/whether GAs and EAa can be applied successfully to problems of Optimisation, Navigation, and also other contemporary and emerging fields.
This book emphasizes on a lot of fresh ideas (which already requires background in GA, and Algorithms) and may be highly recommended for reference reading of Evolutionary Algorithms and allied Techniques.
could GA get possibly any eaiser to understand??? Mar 20, 2005
I saw this book once with one of my buddies,and read the first chapter,,,it was after looking up the first chapter i decided to buy it...I have read some other books on this topic,and since i was kinda in rush for a project which needed GA,i found no other book which explains the concepts and procedures, this straightforward and "right to the point".As far as writing this book goes, "Michalewicz" has done a really really great job. Go for it guys!!! cheers, Amir
One of the best book on genetic algorithms Jul 12, 2002
A very good vision of the evolutionary optimisation techniques not only GA. As well there is an excellent chapter on constraints handling. Maybe it is not one of the easiest book on GA but it is definitely the most useful.
pretty bad Jun 13, 2001
I agree with the previous reviewer: books should be clear and get to the point. Forget about this one. Get Michalewicz and Fogel's "How to solve it" book. It is MUCH better than this one in all levels: it is better written and the content is more authorative and helpful to novices and experts.
This book is supposed to be a textbook. Maybe that's why it sells so well. I guess I am lucky I didn't have to take a class with this thing.
Awful, unreadable book. Nov 13, 2000
This man needs to invest in a good editor. Many times I'd read through half a page or so, stop to think about it and then rephrase it into one or two sentences. Blobs of math appear to be thrown in with little justification, and the book isn't improved by them.
But this book is not only unreadable, it's also not useful. It's more an overview of the area than anything else; it doesn't give adequate information about genetic programming or neural networks. It skims many areas in a close to incomprehensible fashion without covering any in what I would consider to be good detail.
Finally, I'm not dim. I have a PhD myself and am used to ploughing through gibberish. But save your money and don't buy this book (Unless you have a wobbly table that needs fixing).