Introduction to Genetic Algorithms

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Springer Science & Business Media, Oct 24, 2007 - Mathematics - 442 pages
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Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses 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

14 Advantages of Evolutionary Computation
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 x
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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