Genetic Algorithms + Data Structures = Evolution ProgramsSpringer Science & Business Media, 21 mrt 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 |
363 | |
383 | |
Overige edities - Alles bekijken
Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz Gedeeltelijke weergave - 2013 |
Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz Geen voorbeeld beschikbaar - 2014 |
Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz Geen voorbeeld beschikbaar - 2011 |
Veelvoorkomende woorden en zinsdelen
applied approach average best individual bits C₁ Chapter chromosome chromosome representation contractive mapping convergence cost crossover crossover operator data structures decoders defined discussed domain edges encoding eval evaluation function evolution process evolution program evolution strategies evolutionary algorithms evolutionary computation evolutionary programming evolutionary techniques example experiments feasible and infeasible feasible solution floating point GAMS GAVAPS gene genetic algorithm genetic operators GENETIC-2 GENOCOP global optimum heuristic implementation incorporate infeasible individuals infeasible solutions initial population integer iteration knapsack linear constraints matrix method minimize mutation mutation operator nodes numerical optimization objective function offspring optimization problems parameters parents path penalty functions performance pop_size potential solutions probability procedure random number randomly recombination repair algorithms represents robot scheduling schema schemata search space selection sequence simulated annealing solution vector solve subtours tion tour transportation problem traveling salesman problem v₁ variables
Verwijzingen naar dit boek
Advanced Parallel Processing Technologies: 7th International Symposium, APPT ... Ming Xu,Yinwei Zhan,Jiannong Cao,Yijun Liu Gedeeltelijke weergave - 2007 |