# Genetic Algorithms + Data Structures = Evolution Programs

Springer Science & Business Media, 1996 - 387 pagina's
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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### Inhoudsopgave

 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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