Preface	5
	About the Book	7
	1 Organization of the Book	7
	2 Salient Features of the Book	8
	Acknowledgement	10
	Contents	11
	Evolutionary Computation	18
	1.1 Introduction	18
	1.2 The Historical Development of EC	19
	1.2.1 Genetic Algorithms	19
	1.2.2 Genetic Programming	20
	1.2.3 Evolutionary Strategies	21
	1.2.4 Evolutionary Programming	22
	1.3 Features of Evolutionary Computation	22
	1.3.1 Particulate Genes and Population Genetics	23
	1.3.2 The Adaptive Code Book	24
	1.3.3 The Genotype/Phenotype Dichotomy	25
	1.4 Advantages of Evolutionary Computation	26
	1.4.1 Conceptual Simplicity	27
	1.4.2 Broad Applicability	27
	1.4.3 Hybridization with Other Methods	28
	1.4.4 Parallelism	28
	1.4.5 Robust to Dynamic Changes	28
	1.4.6 Solves Problems that have no Solutions	29
	1.5 Applications of Evolutionary Computation	29
	1.6 Summary	30
	Review Questions	30
	Genetic Algorithms	31
	2.1 Introduction	31
	2.2 Biological Background	32
	2.2.1 The Cell	32
	2.2.2 Chromosomes	32
	2.2.3 Genetics	33
	2.2.4 Reproduction	33
	2.2.5 Natural Selection	35
	2.3 What is Genetic Algorithm?	36
	2.3.1 Search Space	36
	2.3.2 Genetic Algorithms World	36
	2.3.3 Evolution and Optimization	38
	2.3.4 Evolution and Genetic Algorithms	39
	2.4 Conventional Optimization and Search Techniques	40
	2.4.1 Gradient-Based Local OptimizationMethod	41
	2.4.2 Random Search	42
	2.4.3 Stochastic Hill Climbing	43
	2.4.4 Simulated Annealing	43
	2.4.5 Symbolic Artificial Intelligence (AI)	45
	2.5 A Simple Genetic Algorithm	45
	2.6 Comparison of Genetic Algorithm with Other Optimization Techniques	49
	2.7 Advantages and Limitations of Genetic Algorithm	50
	2.8 Applications of Genetic Algorithm	51
	2.9 Summary	52
	Review Questions	52
	Terminologies and Operators of GA	54
	3.1 Introduction	54
	3.2 Key Elements	54
	3.3 Individuals	54
	3.4 Genes	55
	3.5 Fitness	56
	3.6 Populations	56
	3.7 Data Structures	57
	3.8 Search Strategies	58
	3.9 Encoding	58
	3.9.1 Binary Encoding	58
	3.9.2 Octal Encoding	59
	3.9.3 Hexadecimal Encoding	59
	3.9.4 Permutation Encoding (Real Number Coding)	59
	3.9.5 Value Encoding	60
	3.9.6 Tree Encoding	60
	3.10 Breeding	61
	3.10.1 Selection	61
	3.10.2 Crossover (Recombination)	65
	3.10.3 Mutation	71
	3.10.4 Replacement	72
	3.11 Search Termination (Convergence Criteria)	74
	3.11.1 Best Individual	74
	3.11.2 Worst individual	75
	3.11.3 Sum of Fitness	75
	3.11.4 Median Fitness	75
	3.12 Why do Genetic AlgorithmsWork?	75
	3.12.1 Building Block Hypothesis	76
	3.12.2 A Macro-Mutation Hypothesis	77
	3.12.3 An Adaptive Mutation Hypothesis	77
	3.12.4 The Schema Theorem	78
	3.12.5 Optimal Allocation of Trials	80
	3.12.6 Implicit Parallelism	81
	3.12.7 The No Free Lunch Theorem	83
	3.13 Solution Evaluation	83
	3.14 Search Refinement	84
	3.15 Constraints	84
	3.16 Fitness Scaling	85
	3.16.1 Linear Scaling	85
	3.16.2 Sigma Truncation	86
	3.16.3 Power Law Scaling	87
	3.17 Example Problems	87
	3.17.1 Maximizing a Function	87
	3.17.2 Traveling Salesman Problem	91
	3.18 Summary	93
	Review Questions	95
	Exercise Problems	96
	Advanced Operators and Techniques in Genetic Algorithm	97
	4.1 Introduction	97
	4.2 Diploidy, Dominance and Abeyance	97
	4.3 Multiploid	99
	4.4 Inversion and Reordering	100
	4.4.1 Partially Matched Crossover (PMX)	102
	4.4.2 Order Crossover (OX)	102
	4.4.3 Cycle Crossover (CX)	103
	4.5 Niche and Speciation	103
	4.5.1 Niche and Speciation in Multimodal Problems	104
	4.5.2 Niche and Speciation in Unimodal Problems	107
	4.5.3 Restricted Mating	110
	4.6 Few Micro-operators	111
	4.6.1 Segregation and Translocation	111
	4.6.2 Duplication and Deletion	111
	4.6.3 Sexual Determination	112
	4.7 Non-binary Representation	112
	4.8 Multi-Objective Optimization	113
	4.9 Combinatorial Optimizations	114
	4.10 Knowledge Based Techniques	114
	4.11 Summary	116
	Review Questions	117
	Exercise Problems	117
	Classification of Genetic Algorithm	119
	5.1 Introduction	119
	5.2 Simple Genetic Algorithm (SGA)	119
	5.3 Parallel and Distributed Genetic Algorithm (PGA and DGA)	120
	5.3.1 Master-Slave Parallelization	123
	5.3.2 Fine Grained Parallel GAs (Cellular GAs)	124
	5.3.3 Multiple-Deme Parallel GAs (Distributed GAs or Coarse Grained GAs)	125
	5.3.4 Hierarchical Parallel Algorithms	127
	5.4 Hybrid Genetic Algorithm (HGA)	129
	5.4.1 Crossover	130
	5.4.2 Initialization Heuristics	131
	5.4.3 The RemoveSharp Algorithm	131
	5.4.4 The LocalOpt Algorithm	133
	5.5 Adaptive Genetic Algorithm (AGA)	133
	5.5.1 Initialization	134
	5.5.2 Evaluation Function	134
	5.5.3 Selection operator	135
	5.5.4 Crossover operator	135
	5.5.5 Mutation operator	136
	5.6 Fast Messy Genetic Algorithm (FmGA)	136
	5.6.1 Competitive Template (CT) Generation	137
	5.7 Independent Sampling Genetic Algorithm (ISGA)	138
	5.7.1 Independent Sampling Phase	139
	5.7.2 Breeding Phase	140
	5.8 Summary	141
	Review Questions	142
	Exercise Problems	143
	Genetic Programming	144
	6.1 Introduction	144
	6.2 Comparison of GP with Other Approaches	144
	6.3 Primitives of Genetic Programming	148
	6.3.1 Genetic Operators	149
	6.3.2 Generational Genetic Programming	149
	6.3.3 Tree Based Genetic Programming	149
	6.3.4 Representation of Genetic Programming	150
	6.4 Attributes in Genetic Programming	154
	6.5 Steps of Genetic Programming	156
	6.5.1 Preparatory Steps of Genetic Programming	156
	6.5.2 Executional Steps of Genetic Programming	159
	6.6 Characteristics of Genetic Programming	162
	6.6.1 What We Mean by "Human-Competitive	162
	6.6.2 What We Mean by "High-Return"	165
	6.6.3 What We Mean by "Routine"	167
	6.6.4 What We Mean by "Machine Intelligence"	167
	6.7 Applications of Genetic Programming	169
	6.7.1 Applications of Genetic Programming in Civil Engineering	169
	6.8 Haploid Genetic Programming with Dominance	172
	6.8.1 Single-Node Dominance Crossover	174
	6.8.2 Sub-Tree Dominance Crossover	174
	6.9 Summary	174
	Review Questions	176
	Exercise Problems	176
	Genetic Algorithm Optimization Problems	177
	7.1 Introduction	177
	7.2 Fuzzy Optimization Problems	177
	7.2.1 Fuzzy Multiobjective Optimization	178
	7.2.2 Interactive Fuzzy Optimization Method	180
	7.2.3 Genetic Fuzzy Systems	180
	7.3 Multiobjective Reliability Design Problem	182
	7.3.1 Network Reliability Design	182
	7.3.2 Bicriteria Reliability Design	186
	7.4 Combinatorial Optimization Problem	188
	7.4.1 Linear Integer Model	190
	7.4.2 Applications of Combinatorial Optimization	191
	7.4.3 Methods	194
	7.5 Scheduling Problems	199
	7.5.1 Genetic Algorithm for Job Shop Scheduling Problems (JSSP)	199
	7.6 Transportation Problems	202
	7.6.1 Genetic Algorithm in Solving Transportation Location- Allocation Problems with Euclidean Distances	203
	7.6.2 Real-Coded Genetic Algorithm (RCGA) for Integer Linear Programming in Production- Transportation Problems with Flexible Transportation Cost	206
	7.7 Network Design and Routing Problems	211
	7.7.1 Planning of Passive Optical Networks	211
	7.7.2 Planning of Packet Switched Networks	214
	7.7.3 Optimal Topological Design of All Terminal Networks	215
	7.8 Summary	220
	Review Questions	220
	Exercise Problems	221
	Genetic Algorithm Implementation Using Matlab	222
	8.1 Introduction	222
	8.2 Data Structures	222
	8.2.1 Chromosomes	223
	8.2.2 Phenotypes	223
	8.2.3 Objective Function Values	224
	8.2.4 Fitness Values	224
	8.2.5 Multiple Subpopulations	224
	8.3 Toolbox Functions	225
	8.4 Genetic Algorithm Graphical User Interface Toolbox	230
	8.5 Solved Problems using MATLAB	235
	8.6 Summary	272
	Review Questions	272
	Exercise Problems	273
	Genetic Algorithm Optimization in C/C++	274
	9.1 Introduction	274
	9.2 Traveling Salesman Problem (TSP)	274
	9.3 Word Matching Problem	282
	9.4 PrisonerÌs Dilemma	291
	9.5 Maximize	297
	9.6 Minimization a Sine Function with Constraints	303
	9.6.1 Problem Description	304
	9.7 Maximizing the Function	313
	9.8 Quadratic Equation Solving	321
	9.9 Summary	326
	9.9.1 Projects	326
	Applications of Genetic Algorithms	328
	10.1 Introduction	328
	10.2 Mechanical Sector	328
	10.2.1 Optimizing Cyclic-Steam Oil Productionwith Genetic Algorithms	328
	10.2.2 Genetic Programming and Genetic Algorithms for Auto- tuning Mobile Robot Motion Control	331
	10.3 Electrical Engineering	335
	10.3.1 Genetic Algorithms in Network Synthesis	335
	10.3.2 Genetic Algorithm Tools for Control Systems Engineering	339
	10.3.3 Genetic Algorithm Based Fuzzy Controller for Speed Control of Brushless DC Motor	345
	10.4 Machine Learning	352
	10.4.1 Feature Selection in Machine learning using GA	352
	10.5 Civil Engineering	356
	10.5.1 Genetic Algorithm as Automatic Structural Design Tool	356
	10.5.2 Genetic Algorithm for Solving Site Layout Problem	361
	10.6 Image Processing	363
	10.6.1 Designing Texture Filters with Genetic Algorithms	363
	10.6.2 Genetic Algorithm Based Knowledge Acquisition on Image Processing	368
	10.6.3 Object Localization in Images Using Genetic Algorithm	373
	10.6.4 Problem Description	374
	10.6.5 Image Preprocessing	375
	10.6.6 The Proposed Genetic Algorithm Approach	376
	10.7 Data Mining	378
	10.7.1 A Genetic Algorithm for Feature Selection in Data-Mining	378
	10.7.2 Genetic Algorithm Based Fuzzy Data Mining to Intrusion Detection	381
	10.7.3 Selection and Partitioning of Attributes in Large-Scale Data Mining Problems Using Genetic Algorithm	390
	10.8 Wireless Networks	397
	10.8.1 Genetic Algorithms for Topology Planningin Wireless Networks	397
	10.8.2 Genetic Algorithm for Wireless ATM Network	398
	10.9 Very Large Scale Integration (VLSI)	406
	10.9.1 Development of a Genetic Algorithm Techniquefor VLSI Testing	406
	10.9.2 VLSI Macro Cell Layout Using Hybrid GA	408
	10.9.3 Problem Description	409
	10.9.4 Genetic Layout Optimization	410
	10.10 Summary	413
	Introduction to Particle Swarm Optimization and Ant Colony Optimization	414
	11.1 Introduction	414
	11.2 Particle Swarm Optimization	414
	11.2.1 Background of Particle Swarm Optimization	415
	11.2.2 Operation of Particle Swarm Optimization	416
	11.2.3 Basic Flow of Particle Swarm Optimization	418
	11.2.4 Comparison Between PSO and GA	419
	11.2.5 Applications of PSO	421
	11.3 Ant Colony Optimization	421
	11.3.1 Biological Inspiration	421
	11.3.2 Similarities and Differences Between Real Ants and Artificial Ants	425
	11.3.3 Characteristics of Ant Colony Optimization	426
	11.3.4 Ant Colony Optimization Algorithms	427
	11.3.5 Applications of Ant Colony Optimization	433
	11.4 Summary	435
	Review Questions	435
	Exercise Problems	435
	Web Bibliography	451