Genetic Algorithms in Engineering Systems

By: A. M. S. Zalzala (Editor), Peter Fleming (Editor), A. Chipperfield, J. J. Grefenstette, C. M. Fonseca

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Hardcover | 1 December 1996

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280 Pages

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24.13 x 16.51 x 2.54

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This book comprises ten invited expert contributions on the theory and applications of genetic algorithms in a variety of engineering systems. In addition to addressing the simple formulation of GAs, the chapters include original material on the design of evolutionary algorithms for particular engineering applications. Chosen for their experience in the field, the authors are drawn from both academia and industry worldwide, and provide extensive insight into their respective fields. The volume is suitable for researchers and postgraduates who need to be up-to-date with developments in this important subject, as well as practitioners in industry who are eager to find out how to solve their particular real-life problems.

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Introduction to genetic algorithms	p. 1
What are genetic algorithms?	p. 3
Overview of GAs	p. 3
GAs versus traditional methods	p. 5
Major elements of the GA	p. 5
Population representation and initialisation	p. 6
The objective and fitness functions	p. 8
Selection	p. 9
Roulette wheel selection methods	p. 10
Stochastic universal sampling	p. 12
Crossover (recombination)	p. 12
Multipoint crossover	p. 13
Uniform crossover	p. 13
Other crossover operators	p. 14
Intermediate recombination	p. 14
Line recombination	p. 15
Discussion	p. 15
Mutation	p. 16
Reinsertion	p. 17
Termination of the GA	p. 18
Other evolutionary algorithms	p. 19
Parallel GAs	p. 20
Global GAs	p. 21
Migration GAs	p. 22
Diffusion GAs	p. 26
GAs for engineering systems	p. 30
Example application: gas turbine engine control	p. 33
Problem specification	p. 34
EA implementation	p. 36
Results	p. 37
Discussion	p. 40
Concluding remarks	p. 40
References	p. 41
Levels of evolution for control systems	p. 46
Introduction	p. 46
Evolutionary algorithms	p. 46
Control system applications	p. 48
Overview	p. 49
Evolutionary learning: parameters	p. 49
Evolutionary learning: data structures	p. 51
Evolutionary learning: program level	p. 52
Knowledge representation	p. 54
Rule strength	p. 54
Mutation operators	p. 55
Crossover in SAMUEL	p. 55
Control applications of SAMUEL	p. 56
Evolutionary algorithms for testing intelligent control systems	p. 57
Summary	p. 60
Acknowledgment	p. 60
References	p. 60
Multiobjective genetic algorithms	p. 63
Multiobjective optimisation and preference articulation	p. 64
How do MOGAs differ from simple GAs?	p. 64
Scale-independent decision strategies	p. 65
Cost to fitness mapping and selection	p. 67
Sharing	p. 67
Mating restriction	p. 70
Interactive optimisation and changing environments	p. 70
Putting it all together	p. 70
Experimental results	p. 73
Concluding remarks	p. 74
Acknowledgment	p. 76
References	p. 76
Constraint resolutions in genetic algorithms	p. 79
Introduction	p. 79
Constraint resolution in genetic algorithms	p. 79
Problems in encoding of constraints	p. 82
Fuzzy encoding of contraints	p. 83
Fuzzy logic	p. 84
Membership	p. 84
Rules	p. 86
Defuzzification	p. 87
Example	p. 87
Advantages of fuzzy logic	p. 89
Uses of fuzzy logic	p. 90
Fuzzy logic to resolve constraints in genetic algorithms	p. 90
Engineering applications of the technique [9]	p. 95
Discussion	p. 97
Acknowledgments	p. 98
References	p. 98
Towards the evolution of scaleable neural architectures	p. 99
Introduction	p. 99
Encoding neural networks in chromosomes	p. 100
Evolutionary algorithms	p. 103
Active weights and the simulation of neural networks	p. 105
A set based chromosome structure	p. 107
Set interconnections	p. 108
Example chromosome	p. 108
Results	p. 111
Scaleability	p. 112
Conclusions	p. 113
Acknowledgment	p. 114
References	p. 114
Chaotic systems identification	p. 118
Background	p. 119
Chua's oscillator	p. 119
Synchronisation of nonlinear systems	p. 121
Genetic algorithms	p. 123
Synchronisation-based identification	p. 124
Description of the algorithm	p. 124
Identification of Chua's oscillator	p. 126
Experimental examples	p. 127
Conclusions	p. 131
References	p. 132
Job shop scheduling	p. 134
Introduction	p. 134
Disjunctive graph	p. 135
Active schedules	p. 137
Binary representation	p. 138
Local harmonisation	p. 139
Global harmonisation	p. 140
Forcing	p. 140
Permutaion representation	p. 141
Subsequence exchange crossover	p. 141
Permuation with repetition	p. 142
Heuristic crossover	p. 143
GT crossover	p. 144
Genetic enumeration	p. 145
Priority rule based GA	p. 145
Shifting bottleneck based GA	p. 146
Genetic local search	p. 147
Neighbourhood search	p. 147
Multistep crossover fusion	p. 148
Neighbourhood structures for the JSSP	p. 150
Scheduling in the reversed order	p. 152
MSXF-GA for the job shop scheduling	p. 154
Benchmark problems	p. 155
Muth and Thompson benchmark	p. 155
The ten tough benchmark problems	p. 156
Other heuristic methods	p. 158
Conclusions	p. 158
References	p. 158
Evolutionary algorithms for robotic systems: principles and implementations	p. 161
Optimal motion of industrial robot arms	p. 162
Formulation of the problem	p. 163
Simulation of case studies	p. 165
A two DOF arm	p. 165
A six DOF arm	p. 167
Parallel genetic algorithms	p. 169
A comparative study of the optimisation of cubic polynomial robot motion	p. 170
Background	p. 170
Motion based on cubic splines	p. 171
The genetic formulations	p. 171
The objective functions	p. 172
Pareto-based GA	p. 172
Weighted-sum GA	p. 172
Parameter initialisation	p. 173
Evaluating the population	p. 174
Ranking	p. 174
Fitness assignment	p. 174
Sharing scheme	p. 175
Selection scheme	p. 175
Shuffling	p. 175
Recombination mechanisms	p. 175
Modified feasable solution converter	p. 176
Time intervals mutation	p. 177
Simulation results	p. 177
Case 1: Pareto-based GA	p. 178
Case 2: Pareto-GA versus flexible polyhedron search	p. 180
Case 3: weighted-sum GA	p. 180
Multiple manipulator systems	p. 182
Problem formulation	p. 183
Encoding of paths as strings	p. 184
Fitness function	p. 184
The GA operators	p. 186
Simulation results for two 3DOF arms	p. 187
Mobile manipulator system with nonholonomic constraints	p. 190
Multicriteria cost function	p. 191
Parameter encoding using polynomials	p. 192
Fitness function	p. 193
Genetic evolution	p. 193
Simulation results	p. 194
Discussions and conclusions	p. 195
Acknowledgment	p. 197
References	p. 198
Appendix	p. 199
Motion based on cubic splines	p. 199
Physical limits	p. 201
The feasable solution converter (time scaling)	p. 202
Aerodynamic inverse optimisation problems	p. 203
Direct optimisation of airfoil	p. 206
Approximation concept	p. 206
Results of direct optimisation	p. 206
Inverse optimisation of the airfoil	p. 210
Coding	p. 210
Simple GA with real number coding	p. 212
Fitness evaluation: objective and constraints	p. 213
Construction of fitness function	p. 214
Inverse design cycle	p. 215
Results of airfoil design	p. 217
Inverse optimisation of the wing	p. 218
Pressure distribution for the wing	p. 219
MOGA	p. 220
Results of wing design	p. 221
Summary	p. 225
References	p. 226
Genetic design of VLSI layouts	p. 229
Introduction	p. 229
Physical VLSI design	p. 230
Macro cell layouts	p. 231
Placement	p. 233
Routing	p. 233
Previous genetic approaches	p. 235
A GA for combined placement and routing	p. 236
The genotype representation	p. 237
Floorplanning	p. 238
Integration of routing	p. 239
Computation of the global routes	p. 239
Hybrid creation of the initial population	p. 241
Crossover	p. 242
Mutation	p. 242
Selection	p. 245
Results	p. 245
Conclusions	p. 249
Acknowledgments	p. 251
References	p. 252
Index	p. 254
Table of Contents provided by Syndetics. All Rights Reserved.

Genetic Algorithms in Engineering Systems

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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