Evolutionary Computation 2: Advanced Algorithms and Operators (Google eBook)

Front Cover
Thomas Baeck, D.B Fogel, Z Michalewicz
CRC Press, Nov 20, 2000 - Mathematics - 270 pages
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Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.
  

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Contents

Introduction to fitness evaluation
1
12 Related problems
2
References
3
Encoding and decoding functions
4
23 Gray coded strings
6
24 Messy coding
7
26 Coding for binary variables
9
29 Conclusions
10
152 Theories of natural evolution
102
153 The island model
105
154 The island model genetic algorithm applied to a VLSI design problem
108
155 The influence of island model parameters on evolution
113
156 Final remarks and conclusions
119
References
122
Diffusion cellular models
125
162 Diffusion model implementation techniques
126

Competitive fitness evaluation
12
References
14
Complexitybased fitness evaluation
15
minimumdescriptionlengthbased fitness evaluation for genetic programming
17
44 Recent studies on complexitybased fitness
22
45 Conclusion
23
References
24
Multiobjective optimization
25
53 Current evolutionary approaches to multiobjective optimization
26
54 Concluding remarks
35
References
36
Introduction to constrainthandling techniques
38
Reference
40
Penalty functions
41
72 Static penalty functions
43
73 Dynamic penalty functions
44
74 Adaptive penalty functions
45
75 Future directions in penalty functions
47
Decoders
49
83 Formal description
50
References
55
Repair algorithms
56
92 First example
58
93 Second example
59
94 Conclusion
61
Constraintpreserving operators
62
103 Nonlinear optimization with linear constraints
64
104 Traveling salesman problem
65
References
68
Other constrainthandling methods
69
113 Coevolutionary model approach
70
114 Cultural algorithms
71
115 Segregated genetic algorithm
72
116 Genocop III
73
Constraintsatisfaction problems
75
122 Free optimization constrained optimization and constraint satisfaction
76
123 Transforming constraintsatisfaction problems to evolutionaryalgorithmsuited problems
77
124 Solving the transformed problem
82
125 Conclusions
83
References
84
Niching methods
87
133 Crowding
89
134 Theory
90
References
91
Speciation methods
93
142 Bookers taxonexemplar scheme
94
143 The tagtemplate method
95
144 Phenotypic and genotypic mating restriction
96
145 Speciation using tag bits
97
146 Relationship with parallel algorithms
99
Island migration models evolutionary algorithms based on punctuated equilibria
101
163 Theoretical research in diffusion models
131
164 Conclusion
132
Population sizing
134
172 Sizing for optimal schema processing
135
173 Sizing for accurate schema sampling
136
174 Final comments
139
References
140
Mutation parameters
142
182 Mutation parameters for selfadaptation
143
183 Mutation parameters for direct schedules
144
184 Summary
150
Recombination parameters
152
192 Genotypiclevel recombination
153
193 Phenotypiclevel recombination
157
194 Control of recombination parameters
159
195 Discussion
164
References
166
Parameter control
170
References
186
Selfadaptation
188
212 Mutation operators
189
213 Recombination operators
205
214 Conclusions
208
References
209
Metaevolutionary approaches
212
222 Formal description
214
223 Pseudocode
215
224 Parameter settings
216
225 Theory
217
227 Conclusions
220
References
221
Coevolutionary algorithms
224
232 Competitive fitness
225
233 Coevolving sorting networks
227
234 A general coevolutionary genetic algorithm
228
235 Discussion
234
References
236
Efficient implementation of algorithms
239
243 The selection operator
242
245 The evaluation phase
243
References
246
Computation time of evolutionary operators
247
253 Computation time of mutation operators
250
254 Computation time of recombination operators
251
Hardware realizations of evolutionary algorithms
253
263 Dedicated hardware implementations for evolutionary algorithms
256
264 Evolvable hardware
258
265 Conclusion
261
Further reading
263
Index Volume 2
265
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Page 223 - A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems," in Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, (T.

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