Genetic Algorithms in Engineering Systems

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Overview

The contributions presented in this book are extended version of commissioned papers from some of the highest quality contributions to the conference. Chosen for their experience in the field, the authors are drawn from academia and industry worldwide. The chapters cover the main fields of work as well as presenting tutorial material in this important subject, which is currently receiving considerable attention from engineers.

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Editorial Reviews

Booknews
Nineteen contributors from academia and industry overview the fundamentals of genetic algorithms, a stochastic global search model of biological evolution; contrast GAs with more traditional computer search and optimization methods, and with multiobjective genetic algorithms MOGAs; discuss new theories regarding constraint resolutions, neural networks, chaotic systems identification, and levels of evolution for control systems; and present engineering/manufacturing applications e.g. aerodynamic wing design, gas turbine engine control, genetic design of VLSI layouts, job shop scheduling, robotics, and signal processing. SAMUEL, a machine learning program, embodies some of the parameters considered. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780852969021
  • Publisher: Institution of Engineering and Technology (IET)
  • Publication date: 2/1/1999
  • Series: Control Engineering Series
  • Pages: 280

Table of Contents

1 Introduction to genetic algorithms 1
1.1 What are genetic algorithms? 3
1.1.1 Overview of GAs 3
1.1.2 GAs versus traditional methods 5
1.2 Major elements of the GA 5
1.2.1 Population representation and initialisation 6
1.2.2 The objective and fitness functions 8
1.2.3 Selection 9
1.2.3.1 Roulette wheel selection methods 10
1.2.3.2 Stochastic universal sampling 12
1.2.4 Crossover (recombination) 12
1.2.4.1 Multipoint crossover 13
1.2.4.2 Uniform crossover 13
1.2.4.3 Other crossover operators 14
1.2.4.4 Intermediate recombination 14
1.2.4.5 Line recombination 15
1.2.4.6 Discussion 15
1.2.5 Mutation 16
1.2.6 Reinsertion 17
1.2.7 Termination of the GA 18
1.3 Other evolutionary algorithms 19
1.4 Parallel GAs 20
1.4.1 Global GAs 21
1.4.2 Migration GAs 22
1.4.3 Diffusion GAs 26
1.5 GAs for engineering systems 30
1.6 Example application: gas turbine engine control 33
1.6.1 Problem specification 34
1.6.2 EA implementation 36
1.6.3 Results 37
1.6.4 Discussion 40
1.7 Concluding remarks 40
1.8 References 41
2 Levels of evolution for control systems 46
2.1 Introduction 46
2.1.1 Evolutionary algorithms 46
2.1.2 Control system applications 48
2.1.3 Overview 49
2.2 Evolutionary learning: parameters 49
2.3 Evolutionary learning: data structures 51
2.4 Evolutionary learning: program level 52
2.4.1 Knowledge representation 54
2.4.2 Rule strength 54
2.4.3 Mutation operators 55
2.4.4 Crossover in SAMUEL 55
2.4.5 Control applications of SAMUEL 56
2.5 Evolutionary algorithms for testing intelligent control systems 57
2.6 Summary 60
2.7 Acknowledgment 60
2.8 References 60
3 Multiobjective genetic algorithms 63
3.1 Multiobjective optimisation and preference articulation 64
3.2 How do MOGAs differ from simple GAs? 64
3.2.1 Scale-independent decision strategies 65
3.2.2 Cost to fitness mapping and selection 67
3.2.3 Sharing 67
3.2.4 Mating restriction 70
3.2.5 Interactive optimisation and changing environments 70
3.3 Putting it all together 70
3.4 Experimental results 73
3.5 Concluding remarks 74
3.6 Acknowledgment 76
3.7 References 76
4 Constraint resolutions in genetic algorithms 79
4.1 Introduction 79
4.2 Constraint resolution in genetic algorithms 79
4.3 Problems in encoding of constraints 82
4.4 Fuzzy encoding of contraints 83
4.5 Fuzzy logic 84
4.5.1 Membership 84
4.5.2 Rules 86
4.5.3 Defuzzification 87
4.5.4 Example 87
4.5.5 Advantages of fuzzy logic 89
4.5.6 Uses of fuzzy logic 90
4.6 Fuzzy logic to resolve constraints in genetic algorithms 90
4.7 Engineering applications of the technique [9] 95
4.8 Discussion 97
4.9 Acknowledgments 98
4.10 References 98
5 Towards the evolution of scaleable neural architectures 99
5.1 Introduction 99
5.2 Encoding neural networks in chromosomes 100
5.3 Evolutionary algorithms 103
5.4 Active weights and the simulation of neural networks 105
5.5 A set based chromosome structure 107
5.5.1 Set interconnections 108
5.5.2 Example chromosome 108
5.5.3 Results 111
5.5.4 Scaleability 112
5.6 Conclusions 113
5.7 Acknowledgment 114
5.8 References 114
6 Chaotic systems identification 118
6.1 Background 119
6.1.1 Chua's oscillator 119
6.1.2 Synchronisation of nonlinear systems 121
6.1.3 Genetic algorithms 123
6.2 Synchronisation-based identification 124
6.2.1 Description of the algorithm 124
6.2.2 Identification of Chua's oscillator 126
6.3 Experimental examples 127
6.4 Conclusions 131
6.5 References 132
7 Job shop scheduling 134
7.1 Introduction 134
7.2 Disjunctive graph 135
7.2.1 Active schedules 137
7.3 Binary representation 138
7.3.1 Local harmonisation 139
7.3.2 Global harmonisation 140
7.3.3 Forcing 140
7.4 Permutaion representation 141
7.4.1 Subsequence exchange crossover 141
7.4.2 Permuation with repetition 142
7.5 Heuristic crossover 143
7.5.1 GT crossover 144
7.6 Genetic enumeration 145
7.6.1 Priority rule based GA 145
7.6.2 Shifting bottleneck based GA 146
7.7 Genetic local search 147
7.7.1 Neighbourhood search 147
7.7.2 Multistep crossover fusion 148
7.7.3 Neighbourhood structures for the JSSP 150
7.7.4 Scheduling in the reversed order 152
7.7.5 MSXF-GA for the job shop scheduling 154
7.8 Benchmark problems 155
7.8.1 Muth and Thompson benchmark 155
7.8.2 The ten tough benchmark problems 156
7.9 Other heuristic methods 158
7.10 Conclusions 158
7.11 References 158
Evolutionary algorithms for robotic systems: principles and implementations 161
8.1 Optimal motion of industrial robot arms 162
8.1.1 Formulation of the problem 163
8.1.2 Simulation of case studies 165
8.1.2.1 A two DOF arm 165
8.1.2.2 A six DOF arm 167
8.1.3 Parallel genetic algorithms 169
8.2 A comparative study of the optimisation of cubic polynomial robot motion 170
8.2.1 Background 170
8.2.2 Motion based on cubic splines 171
8.2.3 The genetic formulations 171
8.2.4 The objective functions 172
8.2.4.1 Pareto-based GA 172
8.2.4.2 Weighted-sum GA 172
8.2.5 Parameter initialisation 173
8.2.6 Evaluating the population 174
8.2.6.1 Ranking 174
8.2.6.2 Fitness assignment 174
8.2.6.3 Sharing scheme 175
8.2.7 Selection scheme 175
8.2.8 Shuffling 175
8.2.9 Recombination mechanisms 175
8.2.10 Modified feasable solution converter 176
8.2.11 Time intervals mutation 177
8.2.12 Simulation results 177
8.2.12.1 Case 1: Pareto-based GA 178
8.2.12.2 Case 2: Pareto-GA versus flexible polyhedron search 180
8.2.12.3 Case 3: weighted-sum GA 180
8.3 Multiple manipulator systems 182
8.3.1 Problem formulation 183
8.3.2 Encoding of paths as strings 184
8.3.3 Fitness function 184
8.3.4 The GA operators 186
8.3.5 Simulation results for two 3DOF arms 187
8.4 Mobile manipulator system with nonholonomic constraints 190
8.4.1 Multicriteria cost function 191
8.4.2 Parameter encoding using polynomials 192
8.4.3 Fitness function 193
8.4.4 Genetic evolution 193
8.4.5 Simulation results 194
8.5 Discussions and conclusions 195
8.6 Acknowledgment 197
8.7 References 198
8.8 Appendix 199
8.8.1 Motion based on cubic splines 199
8.8.2 Physical limits 201
8.8.3 The feasable solution converter (time scaling) 202
9 Aerodynamic inverse optimisation problems 203
9.1 Direct optimisation of airfoil 206
9.1.1 Approximation concept 206
9.1.2 Results of direct optimisation 206
9.2 Inverse optimisation of the airfoil 210
9.2.1 Coding 210
9.2.2 Simple GA with real number coding 212
9.2.3 Fitness evaluation: objective and constraints 213
9.2.4 Construction of fitness function 214
9.2.5 Inverse design cycle 215
9.2.6 Results of airfoil design 217
9.3 Inverse optimisation of the wing 218
9.3.1 Pressure distribution for the wing 219
9.3.2 MOGA 220
9.3.4 Results of wing design 221
9.4 Summary 225
9.5 References 226
10 Genetic design of VLSI layouts 229
10.1 Introduction 229
10.2 Physical VLSI design 230
10.2.1 Macro cell layouts 231
10.2.2 Placement 233
10.2.3 Routing 233
10.2.4 Previous genetic approaches 235
10.3 A GA for combined placement and routing 236
10.3.1 The genotype representation 237
10.3.2 Floorplanning 238
10.3.3 Integration of routing 239
10.3.4 Computation of the global routes 239
10.3.5 Hybrid creation of the initial population 241
10.3.6 Crossover 242
10.3.7 Mutation 242
10.3.8 Selection 245
10.4 Results 245
10.5 Conclusions 249
10.6 Acknowledgments 251
10.7 References 252
Index 254
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