Genetic Algorithms + Data Structures = Evolution Programs [Hardcover]

Zbigniew Michalewicz (Author)

Book Description

Publication Date: November 26, 1998

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.

Editorial Reviews

Amazon.com Review

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.

Product Details

• Hardcover: 407 pages
• Publisher: Springer (November 26, 1998)
• Language: English
• ISBN-10: 3540606769
• ISBN-13: 978-3540606765
• Product Dimensions: 9.5 x 6.4 x 1 inches
• Shipping Weight: 1.6 pounds (View shipping rates and policies)
• Average Customer Review: 3.7 out of 5 stars  See all reviews (10 customer reviews)
• Amazon Best Sellers Rank: #317,975 in Books (See Top 100 in Books)
  • #4 in Books > Computers & Technology > Programming > Algorithms > Genetic

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Customer Reviews

Most Helpful 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, August 3, 1998

By A Customer

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 (Pasadena, MD USA) - See all my reviews
(REAL NAME)   

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., November 13, 2000

By A Customer

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).