Genetic Algorithms + Data Structures = Evolution Programs / Edition 3

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More About This Textbook

Overview

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 aim of this book is to talk about the field of evolutionary computation in simple terms, and discuss the simplicity and elegance of its methods on many interesting test cases. The book may serve as a guide to writing an evolution program, and to making this an enjoyable experience. It is self-contained and the only prerequisite is basic undergraduate mathematics. Aimed at researchers, practitioners, and graduate students, it may serve as a text for advanced courses in computer science and artificial intelligence, operations research, and engineering. This third edition has been substantially revised and extended. Three new chapters discuss the recent paradigm of genetic programming, heuristic methods and constraint handling, and current directions of research. Additional appendices contain test functions for experiments with evolutionary techniques and discuss possible projects for use in a project-oriented course.
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Product Details

  • ISBN-13: 9783540606765
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 11/26/1998
  • Edition description: 3rd, rev. and extended ed. 1996. Corr. 2nd printing 1998
  • Edition number: 3
  • Pages: 408
  • Sales rank: 1,278,200
  • Product dimensions: 6.39 (w) x 9.54 (h) x 1.00 (d)

Table of Contents

Introduction 1
1 GAs: What Are They? 13
2 GAs: How Do They Work? 33
3 GAs: Why Do They Work? 45
4 GAs: Selected Topics 57
5 Binary or Float? 97
6 Fine Local Tuning 107
7 Handling Constraints 121
8 Evolution Strategies and Other Methods 159
9 The Transportation Problem 181
10 The Traveling Salesman Problem 209
11 Evolution Programs for Various Discrete Problems 239
12 Machine Learning 267
13 Evolutionary Programming and Genetic Programming 283
14 A Hierarchy of Evolution Programs 289
15 Evolution Programs and Heuristics 307
16 Conclusions 329
Appendix A 337
Appendix B 349
Appendix C 353
Appendix D 359
References 363
Index 383
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  • Anonymous

    Posted June 12, 2001

    disappointing

    I read Michalewicz and Fogel's 'How to solve it...' first, and that was a big mistake because that is a great book. This is wordy an unclear. Definitely go and read any of the other fine textbooks (Goldgerg or Mitchell). Don't waste you money on this one.

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