Genetic Algorithms + Data Structures = Evolution Programs

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Springer Science & Business Media, Jan 1, 1996 - Business & Economics - 387 pages
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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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excellent book ,provides all that are need to understand the GAs effectively

Contents

Introduction
1
Genetic Algorithms
11
GAs What Are They?
13
11 Optimization of a simple function
18
111 Representation
19
112 Initial population
20
114 Genetic operators
21
116 Experimental results
22
731 Five test cases
144
732 Experiments
147
74 Other possibilities
150
75 GENOCOP III
154
Evolution Strategies and Other Methods
159
81 Evolution of evolution strategies
160
82 Comparison of evolution strategies and genetic algorithms
164
83 Multimodal and multiobjective function optimization
168

121 Representing a strategy
23
123 Experimental results
24
13 Traveling salesman problem
25
14 Hill climbing simulated annealing and genetic algorithms
26
15 Conclusions
30
GAs How Do They Work?
33
GAs Why Do They Work?
45
GAs Selected Topics
57
41 Sampling mechanism
58
42 Characteristics of the function
65
43 Contractive mapping genetic algorithms
68
44 Genetic algorithms with varying population size
72
45 Genetic algorithms constraints and the knapsack problem
80
451 The 01 knapsack problem and the test data
81
452 Description of the algorithms
82
453 Experiments and results
84
46 Other ideas
88
Numerical Optimization
95
Binary or Float?
97
51 The test case
100
522 The floating point implementation
101
532 Nonuniform mutation
103
533 Other operators
104
54 Time performance
105
6 Fine Local Tuning
107
61 The test cases
108
611 The linearquadratic problem
109
613 The pushcart problem
110
621 The representation
111
63 Experiments and results
113
64 Evolution program versus other methods
114
642 The harvest problem
115
644 The significance of nonuniform mutation
117
65 Conclusions
118
Handling Constraints
121
the GENOCOP system
122
711 An example
125
712 Operators
127
713 Testing GENOCOP
130
GENOCOP II
134
73 Other techniques
141
832 Multiobjective optimization
171
84 Other evolution programs
172
Evolution Programs
179
The Transportation Problem
181
911 Classical genetic algorithms
183
912 Incorporating problemspecific knowledge
185
913 A matrix as a representation structure
188
914 Conclusions
194
92 The nonlinear transportation problem
196
925 Parameters
198
927 Experiments and results
201
928 Conclusions
206
The Traveling Salesman Problem
209
Evolution Programs for Various Discrete Problems
239
112 The timetable problem
246
113 Partitioning objects and graphs
247
114 Path planning in a mobile robot environment
253
115 Remarks
261
12 Machine Learning
267
121 The Michigan approach
270
122 The Pitt approach
274
the GIL system
276
1232 Genetic operators
277
124 Comparison
280
125 REGAL
281
Evolutionary Programming and Genetic Programming
283
132 Genetic programming
285
A Hierarchy of Evolution Programs
289
Evolution Programs and Heuristics
307
a summary
309
152 Feasible and infeasible solutions
312
153 Heuristics for evaluating individuals
314
Conclusions
329
Appendix A
337
Appendix B
349
Appendix C
353
Appendix D
359
References
363
Index
383
Copyright

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About the author (1996)

Michalewicz, University of North Carolina at Charlotte