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Genetic Algorithms + Data Structures = Evolution Programs [Hardcover]

Zbigniew Michalewicz (Author)

5.0 out of 5 stars See all reviews (2 customer reviews)

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Genetic Algorithms + Data Structures = Evolution Programs 5.0 out of 5 stars (2)
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Product Details

Hardcover: 250 pages
Publisher: Springer; 1 edition (Feb 24 1993)
Language: English
ISBN-10: 3540553878
ISBN-13: 978-3540553878

Average Customer Review: 5.0 out of 5 stars See all reviews (2 customer reviews)

Amazon Bestsellers Rank: #2,499,542 in Books (See Top 100 in Books)

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Product Description

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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. --This text refers to an alternate Hardcover edition.

Book Description

This book discusses a class of algorithms which rely on analogies to natural processes - algorithms based on the principle of evolution, i.e., survival of the fittest. In these algorithms, called evolution programs, a population of individuals undergo a sequence of transformations. The individuals strive for survival: a selection scheme biased towards fitter individuals selects the next generation. After some generations, the program converges and the best individual hopefully represents the optimum solution. Hence evolution programming techniques are applicable to various hard optimization problems. The book discusses constrained optimization problems in the areas of optimal control, operations research, and engineering. The problems include optimization of functions with linear constraints, the traveling salesman problem, scheduling and partitioning problems, etc. All methods are illustrated by results obtained from various experimental systems. The book collects, in a unified and comprehensive manner, the results of evolution programming techniques previously available only in widely scattered research papers. The importance of these techniques has been growing in the last decade, 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. It is aimed at researchers, practitioners, and graduate students in the areas of computer science (especially artificial intelligence), operations research, and engineering.

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There is a large class of interesting problems for which no reasonably fast algorithms have been developed. Read the first page

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2 Reviews

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5.0 out of 5 stars could GA get possibly any eaiser to understand???, Mar 19 2005

Amir (L'ile-Bizard, QC, Canada) - See all my reviews
(REAL NAME)

This review is from: Genetic Algorithms + Data Structures = Evolution Programs (Hardcover)

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

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5.0 out of 5 stars One of the best book on genetic algorithms, July 11 2002

Jean-Michel Rogero (Cranfield, UK) - See all my reviews

This review is from: Genetic Algorithms + Data Structures = Evolution Programs (Hardcover)

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.

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Most Helpful Customer Reviews on Amazon.com ^(beta)

Amazon.com: 3.7 out of 5 stars (10 customer reviews)

9 of 10 people found the following review helpful

4.0 out of 5 stars It offer a useful way for numerical optimization, Aug 3 1998

By A Customer - Published on Amazon.com

This review is from: Genetic Algorithms + Data Structures = Evolution Programs (Hardcover)

It is not a textbook for genetic algorithm but is useful to those whose research domain is numerical optimization which is widely appeared in engineering area. It is the only book describing a float-number based GA whose importance has not been noticed by many researchers. I think it is a natural way for problem of engineering optimization.

17 of 21 people found the following review helpful

2.0 out of 5 stars Not a good overview text of the subject., July 19 1998

By J. W. Sheppard - Published on Amazon.com

This review is from: Genetic Algorithms + Data Structures = Evolution Programs (Hardcover)

I used this book as the primary text for a graduate course on evolutionary computation. I was looking for a book that provided a good introduction to genetic algorithms and provided a wide cross-section of related algorithms and applications. At the time, this was the only book of its type on the market (other than Goldberg's book). The first couple of chapters (What is a GA, Why Does a GA Work) were pretty good, but then the strong focus of the remainder of the book on numerical optimization resulted in a loss of interest on the part of my students. Since then, I have had to re-organize the class to provide topics such as genetic programming, evolving neural systems, co-evolution, and artificial life -- none of which are covered adequately (if at all) in this book.

22 of 28 people found the following review helpful

1.0 out of 5 stars Awful, unreadable book., Nov 13 2000

By A Customer - Published on Amazon.com

This review is from: Genetic Algorithms + Data Structures = Evolution Programs (Hardcover)

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).

› Go to Amazon.com to see all 10 reviews 3.7 out of 5 stars