Springer; 3rd edition | ISBN: 3540606769 | November 26, 1998 | PDF | 387 pages | 15 Mb
The books is aimed at a large audience: graduate students, programmers, researchers, engineers, designers - everyone who faces challenging optimization problems. In particular, the book will be of interest to the Operations Research community, since many of the problems considered (traveling, salesman, scheduling, transportation problems) are from their area of interest. An understanding of introductory college level mathematics and of basic concept of programming is sufficient to follow all presented material.
Table of Contents:
Part I. Genetic Algorithms
1. GAs: What Are They?
2. GAs: How Do They Work?
3. GAs: How Do They Work?
4. GAs: Selected Topics (Topics about Genetic Algorithm)
Part II. Numerical Optimization
5. Binary or Float
6. Fine Local Tuning
7. Handling Constraints
8. Evolution Strategies and Other Methods
Part III. Evolution Programs
9. The Transportation Problem
10. The Traveling Salesman Problem
11. Evolution Programs for Various Discrete Problems
12. Machine Learning
13. Evolutionary programming and Genetic Programming
14. A Hierarchy of Evolution Programs
15. Evolution Programs and Heuristics
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