## Introduction to Genetic AlgorithmsTheoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed. |

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### Contents

Exercise Problems | 129 |

Genetic Programming | 131 |

63 Primitives of Genetic Programming | 135 |

631 Genetic Operators | 136 |

634 Representation of Genetic Programming | 137 |

64 Attributes in Genetic Programming | 141 |

65 Steps of Genetic Programming | 143 |

652 Executional Steps of Genetic Programming | 146 |

9 | |

141 Conceptual Simplicity | 10 |

143 Hybridization with Other Methods | 11 |

146 Solves Problems that have no Solutions | 12 |

16 Summary | 13 |

Genetic Algorithms 21 Introduction | 15 |

22 Biological Background | 16 |

223 Genetics | 17 |

225 Natural Selection | 19 |

23 What is Genetic Algorithm? | 20 |

233 Evolution and Optimization | 22 |

234 Evolution and Genetic Algorithms | 23 |

24 Conventional Optimization and Search Techniques | 24 |

241 GradientBased Local Optimization Method | 25 |

242 Random Search | 26 |

243 Stochastic Hill Climbing | 27 |

245 Symbolic Artiﬁcial Intelligence AI | 29 |

26 Comparison of Genetic Algorithm with Other Optimization Techniques | 33 |

27 Advantages and Limitations of Genetic Algorithm | 34 |

28 Applications of Genetic Algorithm | 35 |

29 Summary | 36 |

Terminologies and Operators of GA 31 Introduction | 39 |

34 Genes | 40 |

35 Fitness | 41 |

37 Data Structures | 42 |

38 Search Strategies | 43 |

392 Octal Encoding | 44 |

395 Value Encoding | 45 |

310 Breeding | 46 |

3102 Crossover Recombination | 50 |

3103 Mutation | 56 |

3104 Replacement | 57 |

311 Search Termination Convergence Criteria | 59 |

3112 Worst individual | 60 |

3121 Building Block Hypothesis | 61 |

3122 A MacroMutation Hypothesis | 62 |

3124 The Schema Theorem | 63 |

3125 Optimal Allocation of Trials | 65 |

3126 Implicit Parallelism | 66 |

3127 The No Free Lunch Theorem | 68 |

314 Search Reﬁnement | 69 |

316 Fitness Scaling | 70 |

3162 Sigma Truncation | 71 |

317 Example Problems | 72 |

3172 Traveling Salesman Problem | 76 |

318 Summary | 78 |

Exercise Problems | 81 |

Advanced Operators and Technique in Genetic Algorithm | 83 |

43 Multiploid | 85 |

44 Inversion and Reordering | 86 |

441 Partially Matched Crossover PMX | 88 |

443 Cycle Crossover CX | 89 |

451 Niche and Speciation in Multimodal Problems | 90 |

452 Niche and Speciation in Unimodal Problems | 93 |

453 Restricted Mating | 96 |

46 Few Microoperators | 97 |

463 Sexual Determination | 98 |

48 MultiObjective Optimization | 99 |

49 Combinatorial Optimizations | 100 |

411 Summary | 102 |

Exercise Problems | 103 |

Classiﬁcation of Genetic Algorithm | 105 |

53 Parallel and Distributed Genetic Algorithm PGA and DGA | 106 |

531 MasterSlave Parallelization | 109 |

532 Fine Grained Parallel GAs Cellular GAs | 110 |

533 MultipleDeme Parallel GAs Distributed GAs or Coarse Grained GAs | 111 |

534 Hierarchical Parallel Algorithms | 113 |

54 Hybrid Genetic Algorithm HGA | 115 |

541 Crossover | 116 |

542 Initialization Heuristics | 117 |

544 The LocalOpt Algorithm | 119 |

551 Initialization | 120 |

553 Selection operator | 121 |

555 Mutation operator | 122 |

561 Competitive Template CT Generation | 123 |

57 Independent Sampling Genetic Algorithm ISGA | 124 |

571 Independent Sampling Phase | 125 |

572 Breeding Phase | 126 |

58 Summary | 127 |

66 Characteristics of Genetic Programming | 149 |

662 What We Mean by HighReturn | 152 |

663 What We Mean by Routine | 154 |

67 Applications of Genetic Programming | 156 |

68 Haploid Genetic Programming with Dominance | 159 |

681 SingleNode Dominance Crossover | 161 |

Exercise Problems | 163 |

Genetic Algorithm Optimization Problems | 165 |

721 Fuzzy Multiobjective Optimization | 166 |

722 Interactive Fuzzy Optimization Method | 168 |

73 Multiobjective Reliability Design Problem | 170 |

732 Bicriteria Reliability Design | 174 |

74 Combinatorial Optimization Problem | 176 |

741 Linear Integer Model | 178 |

742 Applications of Combinatorial Optimization | 179 |

743 Methods | 182 |

75 Scheduling Problems | 187 |

76 Transportation Problems | 190 |

761 Genetic Algorithm in Solving Transportation LocationAllocation Problems with Euclidean Distances | 191 |

762 RealCoded Genetic Algorithm RCGA for Integer Linear Programming in ProductionTransportation Problems with Flexible Transportation Cost | 194 |

77 Network Design and Routing Problems | 199 |

772 Planning of Packet Switched Networks | 202 |

773 Optimal Topological Design of All Terminal Networks | 203 |

78 Summary | 208 |

Exercise Problems | 209 |

Genetic Algorithm Implementation Using Matlab | 211 |

821 Chromosomes | 212 |

823 Objective Function Values | 213 |

83 Toolbox Functions | 214 |

84 Genetic Algorithm Graphical User Interface Toolbox | 219 |

85 Solved Problems using MATLAB | 224 |

86 Summary | 261 |

Exercise Problems | 262 |

Genetic Algorithm Optimization in CC++ | 263 |

93 Word Matching Problem | 271 |

94 Prisoners Dilemma | 280 |

95 Maximize fx x² | 286 |

96 Minimization a Sine Function with Constraints | 292 |

961 Problem Description | 293 |

97 Maximizing the Function fx xsin10Πx + 10 | 302 |

98 Quadratic Equation Solving | 310 |

99 Summary | 315 |

Applications of Genetic Algorithms | 317 |

1022 Genetic Programming and Genetic Algorithms for Autotuning Mobile Robot Motion Control | 320 |

103 Electrical Engineering | 324 |

1032 Genetic Algorithm Tools for Control Systems Engineering | 328 |

1033 Genetic Algorithm Based Fuzzy Controller for Speed Control of Brushless DC Motor | 334 |

104 Machine Learning | 341 |

105 Civil Engineering | 345 |

1052 Genetic Algorithm for Solving Site Layout Problem | 350 |

106 Image Processing | 352 |

1062 Genetic Algorithm Based Knowledge Acquisition on Image Processing | 357 |

1063 Object Localization in Images Using Genetic Algorithm | 362 |

1064 Problem Description | 363 |

1065 Image Preprocessing | 364 |

1066 The Proposed Genetic Algorithm Approach | 365 |

107 Data Mining | 367 |

1072 Genetic Algorithm Based Fuzzy Data Mining to Intrusion Detection | 370 |

1073 Selection and Partitioning of Attributes in LargeScale Data Mining Problems Using Genetic Algorithm | 379 |

108 Wireless Networks | 386 |

1082 Genetic Algorithm for Wireless ATM Network | 387 |

109 Very Large Scale Integration VLSI | 395 |

1092 VLSI Macro Cell Layout Using Hybrid GA | 397 |

1093 Problem Description | 398 |

1094 Genetic Layout Optimization | 399 |

1010 Summary | 402 |

Introduction to Particle Swarm Optimization and Ant Colony Optimization | 403 |

1121 Background of Particle Swarm Optimization | 404 |

1122 Operation of Particle Swarm Optimization | 405 |

1123 Basic Flow of Particle Swarm Optimization | 407 |

1124 Comparison Between PSO and GA | 408 |

1125 Applications of PSO | 410 |

1132 Similarities and Differences Between Real Ants and Artiﬁcial Ants | 414 |

1133 Characteristics of Ant Colony Optimization | 415 |

1134 Ant Colony Optimization Algorithms | 416 |

1135 Applications of Ant Colony Optimization | 422 |

114 Summary | 424 |

Bibliography | 425 |

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### Common terms and phrases

adaptive alleles ants applied approach attributes binary building blocks cell chrom chromosome Colony Optimization combinatorial optimization components constraints convergence cost crossover crossover operator defined dominance edge encoding evaluation evolution evolutionary algorithms evolutionary computation example fitness function fitness value function value fuzzy gene genetic algorithm genetic operators genetic programming genotype global heuristic hybrid IEEE implemented individuals initial population input integer iteration layout linear machine learning mating MATLAB method minimize mutation mutation operators niches nodes objective function offspring optimal solution optimization problems output parallel GAs parameters parents Particle Swarm Optimization performance phenotype pheromone probability random randomly recombination reliability representation represents reproduction Research scheduling problems schemata scheme search space selection shown in Fig simulation solve specific Step stochastic strategy string structure switches techniques terminal tion Toolbox topology traveling salesman problem tree variables vector