Genetic Algorithms in Engineering Systems 0852969023, 9780852969021
Arising out of the highly successful 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems:
310 70 343KB
English Pages xvi+264 [282] Year 1999
Table of contents :
Genetic algorithms in engineering systems......Page 4
Contents......Page 6
Preface......Page 12
Contributors......Page 16
1 A. Chipperfield: Introduction to genetic algorithms......Page 18
1.1.1 Overview of GAs......Page 20
1.2 Major elements of the GA......Page 22
1.2.1 Population representation and initialisation......Page 23
1.2.2 The objective and fitness functions......Page 25
1.2.3 Selection......Page 26
1.2.3.1 Roulette wheel selection methods......Page 27
1.2.4 Crossover (recombination)......Page 29
1.2A.2 Uniform crossover......Page 30
1.2.4.4 Intermediate recombination......Page 31
1.2.4.6 Discussion......Page 32
1.2.5 Mutation......Page 33
1.2.6 Reinsertion......Page 34
1.2.7 Termination of the GA......Page 35
1.3 Other evolutionary algorithms......Page 36
1.4 Parallel GAs......Page 37
1.4.1 Global GAs......Page 38
1.4.2 Migration GAs......Page 39
1.4.3 Diffusion GAs......Page 43
1.5 GAs for engineering systems......Page 47
1.6 Example application: gas turbine engine control......Page 50
1.6.1 Problem specification......Page 51
1.6.2 EA implementation......Page 53
1.6.3 Results......Page 54
1.7 Concluding remarks......Page 57
1.8 References......Page 58
2.1.1 Evolutionary algorithms......Page 63
2.1.2 Control system applications......Page 65
2.2 Evolutionary learning: parameters......Page 66
2.3 Evolutionary learning: data structures......Page 68
2.4 Evolutionary learning: program level......Page 69
2.4.2 Rule strength......Page 71
2.4.4 Crossover in SAMUEL......Page 72
2.4.5 Control applications of SAMUEL......Page 73
2.5 Evolutionary algorithms for testing intelligent control systems......Page 74
2.8 References......Page 77
3 C. M. Fonseca and P. J. Fleming: Multiobjective genetic algorithms......Page 80
3.2 How do MOGAs differ from simple GAs?......Page 81
3.2.1 Scale-independent decision strategies......Page 82
3.2.3 Sharing......Page 84
3.3 Putting it all together......Page 87
3.4 Experimental results......Page 90
3.5 Concluding remarks......Page 91
3.7 References......Page 93
4.2 Constraint resolution in genetic algorithms......Page 96
4.3 Problems in encoding of constraints......Page 99
4.4 Fuzzy encoding of constraints......Page 100
4.5.1 Membership......Page 101
4.5.2 Rules......Page 103
4.5.4 Example......Page 104
4.5.5 Advantages of fuzzy logic......Page 106
4,6 Fuzzy logic to resolve constraints in genetic algorithms......Page 107
4.7 Engineering applications of the technique [9]......Page 112
4.8 Discussion......Page 114
4.10 References......Page 115
5.1 Introduction......Page 116
5.2 Encoding neural networks in chromosomes......Page 117
5.3 Evolutionary algorithms......Page 120
5.4 Active weights and the simulation of neural networks......Page 122
5.5 A set based chromosome structure......Page 124
5.5.2 Example chromosome......Page 125
5.53 Results......Page 128
5.5.4 Scaleability......Page 129
5.6 Conclusions......Page 130
5.8 References......Page 131
6 R. Caponetto, L. Fortuna, M. Lavorgna, G. Manganaro: Chaotic systems identification......Page 135
6.1.1 Chua's oscillator......Page 136
6.1.2 Synchronisation of nonlinear systems......Page 138
6.1.3 Genetic algorithms......Page 140
6.2.1 Description of the algorithm......Page 141
6.2.2 Identification of Chua's oscillator......Page 143
6.3 Experimental examples......Page 144
6.4 Conclusions......Page 148
6.5 References......Page 149
7.1 Introduction......Page 151
7,2 Disjunctive graph......Page 152
7.2.1 Active schedules......Page 154
7.3 Binary representation......Page 155
7.3.1 Local harmonisation......Page 156
7.3.3 Forcing......Page 157
7.4.1 Subsequence exchange crossover......Page 158
7.4.2 Permutation with repetition......Page 159
7.5 Heuristic crossover......Page 160
7.5.1 GT crossover......Page 161
7.6.1 Priority rule based GA......Page 162
7.6.2 Shifting bottleneck based GA......Page 163
7.7.1 Neighbourhood search......Page 164
7.7.2 Multistep crossover fusion......Page 165
7.7.3 Neighbourhood structures for the JSSP......Page 167
7.7.4 Scheduling in the reversed order......Page 169
7.7.5 MSXF-GA for job shop scheduling......Page 171
7.8.1 Muth and Thompson benchmark......Page 172
7.8.2 The ten tough benchmark problems......Page 173
7.11 References......Page 175
8 A. M. S. Zalzala, M C. Ang, M. Chen, A. S. Rana and Q. Wang: Evolutionary algorithms for robotic systems: principles and implementations......Page 178
8.1 Optimal motion of industrial robot arms......Page 179
8.1.1 Formulation of the problem......Page 180
8.1.2.1 A two DOF arm......Page 182
8.1.2.2 A six DOF arm......Page 184
8.1.3 Parallel genetic algorithms......Page 186
8.2.1 Background......Page 187
8.2.3 The genetic formulations......Page 188
8.2.4.2 Weighted-sum GA......Page 189
8.2.5 Parameter initialisation......Page 190
8.2.6.2 Fitness assignment......Page 191
8.2.9 Recombination mechanisms......Page 192
8.2.10 Modified feasible solution converter......Page 193
8.2.12 Simulation results......Page 194
8.2.12.1 Case 1: Pareto-based GA......Page 195
8.2.12.3 Case 3: weighted-sum GA......Page 197
8.3 Multiple manipulator systems......Page 199
8.3.1 Problem formulation......Page 200
8.3.3 Fitness function......Page 201
8.3.4 The GA operators......Page 203
8.3.5 Simulation results for two 3DOF arms......Page 204
8.4 Mobile manipulator system with nonholonomic constraints......Page 207
8.4.1 Multicriteria cost function......Page 208
8.4.2 Parameter encoding using polynomials......Page 209
8.4.4 Genetic evolution......Page 210
8.4.5 Simulation results......Page 211
8.5 Discussions and conclusions......Page 212
8.6 Acknowledgment......Page 214
8.7 References......Page 215
8,8.1 Motion based on cubic splines......Page 216
8.8.2 Physical limits......Page 218
8.8.3 The feasible solution converter (time scaling)......Page 219
9 S. Obayashi: Aerodynamic inverse optimisation problems......Page 220
9.1.2 Results of direct optimisation......Page 223
9.2.1 Coding......Page 227
9,2.2 Simple GA with real number coding......Page 229
9.2.3 Fitness evaluation: objective and constraints......Page 230
9.2.4 Construction of fitness function......Page 231
9.2.5 Inverse design cycle......Page 232
9.2.6 Results of airfoil design......Page 234
9,3 Inverse optimisation of the wing......Page 235
9.3.1 Pressure distribution for the wing......Page 236
9.3.2 MOGA......Page 237
9.3.3 Results of wing design......Page 238
9.4 Summary......Page 242
9.5 References......Page 243
10.1 Introduction......Page 246
10.2 Physical VLSI design......Page 247
10.2.1 Macro cell layouts......Page 248
10.2.3 Routing......Page 250
10.2.4 Previous genetic approaches......Page 252
10.3 A GA for combined placement and routing......Page 253
10.3.1 The genotype representation......Page 254
10.3.2 Floorplanning......Page 255
10.3.4 Computation of the global routes......Page 256
10.3.5 Hybrid creation of the initial population......Page 258
10.3.7 Mutation......Page 259
10.4 Results......Page 262
10.5 Conclusions......Page 266
10.6 Acknowledgments......Page 268
10.7 References......Page 269
Index......Page 271
Genetic algorithms in engineering systems......Page 4
Contents......Page 6
Preface......Page 12
Contributors......Page 16
1 A. Chipperfield: Introduction to genetic algorithms......Page 18
1.1.1 Overview of GAs......Page 20
1.2 Major elements of the GA......Page 22
1.2.1 Population representation and initialisation......Page 23
1.2.2 The objective and fitness functions......Page 25
1.2.3 Selection......Page 26
1.2.3.1 Roulette wheel selection methods......Page 27
1.2.4 Crossover (recombination)......Page 29
1.2A.2 Uniform crossover......Page 30
1.2.4.4 Intermediate recombination......Page 31
1.2.4.6 Discussion......Page 32
1.2.5 Mutation......Page 33
1.2.6 Reinsertion......Page 34
1.2.7 Termination of the GA......Page 35
1.3 Other evolutionary algorithms......Page 36
1.4 Parallel GAs......Page 37
1.4.1 Global GAs......Page 38
1.4.2 Migration GAs......Page 39
1.4.3 Diffusion GAs......Page 43
1.5 GAs for engineering systems......Page 47
1.6 Example application: gas turbine engine control......Page 50
1.6.1 Problem specification......Page 51
1.6.2 EA implementation......Page 53
1.6.3 Results......Page 54
1.7 Concluding remarks......Page 57
1.8 References......Page 58
2.1.1 Evolutionary algorithms......Page 63
2.1.2 Control system applications......Page 65
2.2 Evolutionary learning: parameters......Page 66
2.3 Evolutionary learning: data structures......Page 68
2.4 Evolutionary learning: program level......Page 69
2.4.2 Rule strength......Page 71
2.4.4 Crossover in SAMUEL......Page 72
2.4.5 Control applications of SAMUEL......Page 73
2.5 Evolutionary algorithms for testing intelligent control systems......Page 74
2.8 References......Page 77
3 C. M. Fonseca and P. J. Fleming: Multiobjective genetic algorithms......Page 80
3.2 How do MOGAs differ from simple GAs?......Page 81
3.2.1 Scale-independent decision strategies......Page 82
3.2.3 Sharing......Page 84
3.3 Putting it all together......Page 87
3.4 Experimental results......Page 90
3.5 Concluding remarks......Page 91
3.7 References......Page 93
4.2 Constraint resolution in genetic algorithms......Page 96
4.3 Problems in encoding of constraints......Page 99
4.4 Fuzzy encoding of constraints......Page 100
4.5.1 Membership......Page 101
4.5.2 Rules......Page 103
4.5.4 Example......Page 104
4.5.5 Advantages of fuzzy logic......Page 106
4,6 Fuzzy logic to resolve constraints in genetic algorithms......Page 107
4.7 Engineering applications of the technique [9]......Page 112
4.8 Discussion......Page 114
4.10 References......Page 115
5.1 Introduction......Page 116
5.2 Encoding neural networks in chromosomes......Page 117
5.3 Evolutionary algorithms......Page 120
5.4 Active weights and the simulation of neural networks......Page 122
5.5 A set based chromosome structure......Page 124
5.5.2 Example chromosome......Page 125
5.53 Results......Page 128
5.5.4 Scaleability......Page 129
5.6 Conclusions......Page 130
5.8 References......Page 131
6 R. Caponetto, L. Fortuna, M. Lavorgna, G. Manganaro: Chaotic systems identification......Page 135
6.1.1 Chua's oscillator......Page 136
6.1.2 Synchronisation of nonlinear systems......Page 138
6.1.3 Genetic algorithms......Page 140
6.2.1 Description of the algorithm......Page 141
6.2.2 Identification of Chua's oscillator......Page 143
6.3 Experimental examples......Page 144
6.4 Conclusions......Page 148
6.5 References......Page 149
7.1 Introduction......Page 151
7,2 Disjunctive graph......Page 152
7.2.1 Active schedules......Page 154
7.3 Binary representation......Page 155
7.3.1 Local harmonisation......Page 156
7.3.3 Forcing......Page 157
7.4.1 Subsequence exchange crossover......Page 158
7.4.2 Permutation with repetition......Page 159
7.5 Heuristic crossover......Page 160
7.5.1 GT crossover......Page 161
7.6.1 Priority rule based GA......Page 162
7.6.2 Shifting bottleneck based GA......Page 163
7.7.1 Neighbourhood search......Page 164
7.7.2 Multistep crossover fusion......Page 165
7.7.3 Neighbourhood structures for the JSSP......Page 167
7.7.4 Scheduling in the reversed order......Page 169
7.7.5 MSXF-GA for job shop scheduling......Page 171
7.8.1 Muth and Thompson benchmark......Page 172
7.8.2 The ten tough benchmark problems......Page 173
7.11 References......Page 175
8 A. M. S. Zalzala, M C. Ang, M. Chen, A. S. Rana and Q. Wang: Evolutionary algorithms for robotic systems: principles and implementations......Page 178
8.1 Optimal motion of industrial robot arms......Page 179
8.1.1 Formulation of the problem......Page 180
8.1.2.1 A two DOF arm......Page 182
8.1.2.2 A six DOF arm......Page 184
8.1.3 Parallel genetic algorithms......Page 186
8.2.1 Background......Page 187
8.2.3 The genetic formulations......Page 188
8.2.4.2 Weighted-sum GA......Page 189
8.2.5 Parameter initialisation......Page 190
8.2.6.2 Fitness assignment......Page 191
8.2.9 Recombination mechanisms......Page 192
8.2.10 Modified feasible solution converter......Page 193
8.2.12 Simulation results......Page 194
8.2.12.1 Case 1: Pareto-based GA......Page 195
8.2.12.3 Case 3: weighted-sum GA......Page 197
8.3 Multiple manipulator systems......Page 199
8.3.1 Problem formulation......Page 200
8.3.3 Fitness function......Page 201
8.3.4 The GA operators......Page 203
8.3.5 Simulation results for two 3DOF arms......Page 204
8.4 Mobile manipulator system with nonholonomic constraints......Page 207
8.4.1 Multicriteria cost function......Page 208
8.4.2 Parameter encoding using polynomials......Page 209
8.4.4 Genetic evolution......Page 210
8.4.5 Simulation results......Page 211
8.5 Discussions and conclusions......Page 212
8.6 Acknowledgment......Page 214
8.7 References......Page 215
8,8.1 Motion based on cubic splines......Page 216
8.8.2 Physical limits......Page 218
8.8.3 The feasible solution converter (time scaling)......Page 219
9 S. Obayashi: Aerodynamic inverse optimisation problems......Page 220
9.1.2 Results of direct optimisation......Page 223
9.2.1 Coding......Page 227
9,2.2 Simple GA with real number coding......Page 229
9.2.3 Fitness evaluation: objective and constraints......Page 230
9.2.4 Construction of fitness function......Page 231
9.2.5 Inverse design cycle......Page 232
9.2.6 Results of airfoil design......Page 234
9,3 Inverse optimisation of the wing......Page 235
9.3.1 Pressure distribution for the wing......Page 236
9.3.2 MOGA......Page 237
9.3.3 Results of wing design......Page 238
9.4 Summary......Page 242
9.5 References......Page 243
10.1 Introduction......Page 246
10.2 Physical VLSI design......Page 247
10.2.1 Macro cell layouts......Page 248
10.2.3 Routing......Page 250
10.2.4 Previous genetic approaches......Page 252
10.3 A GA for combined placement and routing......Page 253
10.3.1 The genotype representation......Page 254
10.3.2 Floorplanning......Page 255
10.3.4 Computation of the global routes......Page 256
10.3.5 Hybrid creation of the initial population......Page 258
10.3.7 Mutation......Page 259
10.4 Results......Page 262
10.5 Conclusions......Page 266
10.6 Acknowledgments......Page 268
10.7 References......Page 269
Index......Page 271
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