Genetic Algorithms + Data Structures = Evolution Programs

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Springer Science & Business Media, 2013 M03 9 - 387 pages
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.
 

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Contents

Introduction
1
What Are They?
13
How Do They Work? 33
32
Why Do They Work?
45
Selected Topics
57
Binary or Float?
97
Fine Local Tuning
107
Handling Constraints
121
The Traveling Salesman Problem
209
Evolution Programs for Various Discrete Problems
239
Machine Learning
267
Evolutionary Programming and Genetic Programming 283
282
A Hierarchy of Evolution Programs
289
Evolution Programs and Heuristics
307
Conclusions 329
328
Appendix B
349

Evolution Strategies and Other Methods 159
158
Evolution Programs
179

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