Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.
Author(s): Nadia Nedjah, Ajith Abraham, Luiza de Macedo Mourelle (Eds.)
Series: Studies in Computational Intelligence 13
Edition: 1
Publisher: Springer
Year: 2006
Language: English
Pages: 255
Tags: Информатика и вычислительная техника;Искусственный интеллект;Эволюционные алгоритмы;
3540298495......Page 1
Contents......Page 10
1 Evolutionary Computation: from Genetic Algorithms to Genetic Programming......Page 21
1.1 Introduction......Page 22
1.2 Genetic Algorithms......Page 23
1.2.1 Encoding and Decoding......Page 24
1.2.2 Schema Theorem and Selection Strategies......Page 25
1.2.3 Reproduction Operators......Page 26
1.3.1 Mutation in Evolution Strategies......Page 29
1.3.3 Controling the Evolution......Page 30
1.4 Evolutionary Programming......Page 31
1.5 Genetic Programming......Page 32
1.5.1 Computer Program Encoding......Page 33
1.5.2 Reproduction of Computer Programs......Page 34
1.6 Variants of Genetic Programming......Page 35
1.6.2 Gene Expression Programming (GEP)......Page 36
1.6.3 Multi Expression Programming......Page 37
1.6.5 Traceless Genetic Programming (TGP)......Page 38
References......Page 39
2.1 Genetic Algorithms: Historical Background......Page 41
2.1.2 Genetic Programming......Page 42
2.1.3 Gene Expression Programming......Page 45
2.2 The Architecture of GEP Individuals......Page 47
2.2.1 Open Reading Frames and Genes......Page 48
2.2.2 Structural Organization of Genes......Page 50
2.2.3 Multigenic Chromosomes and Linking Functions......Page 52
2.3 Chromosome Domains and Random Numerical Constants......Page 53
2.4 Cells and the Creation of Automatically Defined Functions......Page 56
2.4.2 Multicellular Systems......Page 57
2.4.3 Incorporating Random Numerical Constants in ADFs......Page 59
2.5.1 General Settings......Page 60
2.5.2 Results without ADFs......Page 62
2.5.3 Results with ADFs......Page 66
2.6 Summary......Page 74
References......Page 75
3.1 Introduction......Page 77
3.2 Intrusion Detection......Page 78
3.3 Related Research......Page 80
3.4.1 Linear Genetic Programming (LGP)......Page 83
3.4.3 Solution Representation......Page 84
3.5 Machine Learning Techniques......Page 86
3.5.2 Support Vector Machines (SVMs)......Page 87
3.6 Experiment Setup and Results......Page 88
References......Page 97
4 Evolutionary Pattern Matching Using Genetic Programming......Page 100
4.1 Introduction......Page 101
4.2 Preliminary Notation and Terminology......Page 102
4.3 Adaptive Pattern Matching......Page 105
4.3.1 Constructing Adaptive Automata......Page 106
4.3.2 Example of Adaptive Automaton Construction......Page 108
4.4.2 Selecting Partial Indexes......Page 109
4.4.3 A Good Traversal Order......Page 110
4.5 Genetically-Programmed Matching Automata......Page 111
4.5.1 Encoding of adaptive matching automata......Page 112
4.5.3 Genetic Operators......Page 113
4.5.4 Fitness function......Page 118
4.6 Comparative Results......Page 120
4.7 Summary......Page 121
References......Page 122
5.1 Introduction......Page 124
5.2 Genetic Programming in Mathematical Modelling......Page 125
5.2.1 Adaptation of GP to Mathematical Modelling......Page 126
5.2.2 An educational Example – the Hybrid of Genetic Programming and Genetic Algorithm......Page 127
5.2.3 General Remarks......Page 132
5.3 Decision Models for Classification Tasks......Page 133
5.3.1 Adaptation of GP to Classification Task......Page 134
5.3.3 Used Algorithm......Page 136
5.4 GP for Prediction Task and Time Series Odelling......Page 139
5.4.1 Adaptation of GP to Prediction Task......Page 140
5.4.2 Adaptation of GP to Prediction Tasks......Page 141
5.4.3 Hybrid GP and GA to Develope Solar Cycle’s Model......Page 144
5.4.4 General Remarks......Page 146
References......Page 148
6.1 Introduction......Page 150
6.2 Modeling Stock Market Prediction......Page 151
6.3.1 Multi Expression Programming (MEP)......Page 153
6.3.2 Linear Genetic Programming (LGP)......Page 154
6.3.3 Artificial Neural Network (ANN)......Page 155
6.4 Ensemble of GP Techniques......Page 157
6.4.1 Nondominated Sorting Genetic Algorithm II (NSGA II)......Page 158
6.5.1 Parameter Settings......Page 159
6.5.2 Comparisons of Results Obtained by Intelligent Paradigms......Page 162
References......Page 163
7.1 Introduction......Page 166
7.2 Principles of Evolutionary Hardware Design......Page 167
7.3.1 Encoding......Page 171
7.3.2 Genetic Operators......Page 172
7.4.1 Encoding......Page 174
7.4.2 Genetic Operators......Page 177
7.5 Result Comparison......Page 180
References......Page 190
8.1 Introducing Khepera Robot......Page 191
8.2 Evolving Complex Behaviors by Introducing Hierarchy to GP......Page 193
8.2.1 Genetic Programming with Subroutine Library......Page 194
8.2.2 Neural Networks and the Control of Mobile Robots......Page 197
8.2.3 Using Neural Networks as Elements of the Function Set......Page 199
8.2.4 Comments......Page 200
8.3.1 Subsumption Architecture......Page 202
8.3.2 Action Selection Architecture......Page 203
8.3.3 Using GP to Evolve Modules of Subsumption Architecture......Page 204
8.3.4 Using GP to Evolve Modules of Action Selection Architecture......Page 206
8.4 Comments......Page 207
8.5 Summary......Page 208
References......Page 209
9 Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming......Page 210
9.1 Introduction......Page 211
9.2.1 JB Language......Page 212
9.2.3 AIMGP......Page 213
9.2.5 Discussion......Page 214
9.3.1 Microcontrollers......Page 215
9.3.2 Time-Optimal Control......Page 216
9.4 Microcontroller Platform......Page 217
9.5 Linear Genetic Programming......Page 218
9.6.1 Evolutionary Kernel......Page 219
9.6.3 Microcontroller Simulator......Page 223
9.6.5 Overview......Page 224
9.7.1 Cart Centering......Page 225
9.7.2 Inverted Pendulum......Page 235
9.8 Summary......Page 242
References......Page 243
G......Page 245
U......Page 246
Reviewer List......Page 248
