Genetic Systems Programming: Theory and Experiences

Ajith Abraham9783540298496, 3-540-29849-5

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.

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Table of contents :
front-matter.pdf……Page 1
Ajith Abraham, Nadia Nedjah and Luiza de Macedo Mourelle……Page 20
1.1 Introduction……Page 21
1.2 Genetic Algorithms……Page 22
1.3 Evolution Strategies……Page 28
1.4 Evolutionary Programming……Page 30
1.5 Genetic Programming……Page 31
1.6 Variants of Genetic Programming……Page 34
References……Page 38
2.1 Genetic Algorithms: Historical Background……Page 40
2.2 The Architecture of GEP Individuals……Page 46
2.3 Chromosome Domains and Random Numerical Constants……Page 52
2.4 Cells and the Creation of Automatically Defined Functions……Page 55
2.5 Analyzing the Importance of ADFs in Automatic Programming……Page 59
2.6 Summary……Page 73
References……Page 74
3.1 Introduction……Page 76
3.2 Intrusion Detection……Page 77
3.3 Related Research……Page 79
3.4 Evolving IDS Using Genetic Programming (GP)……Page 82
3.5 Machine Learning Techniques……Page 85
3.6 Experiment Setup and Results……Page 87
References……Page 96
Nadia Nedjah and Luiza de Macedo Mourelle……Page 99
4.1 Introduction……Page 100
4.2 Preliminary Notation and Terminology……Page 101
4.3 Adaptive Pattern Matching……Page 104
4.4 Heuristics for Good Traversal Orders……Page 108
4.5 Genetically-Programmed Matching Automata……Page 110
4.6 Comparative Results……Page 119
4.7 Summary……Page 120
References……Page 121
5.1 Introduction……Page 123
5.2 Genetic Programming in Mathematical Modelling……Page 124
5.3 Decision Models for Classification Tasks……Page 132
5.4 GP for Prediction Task and Time Series Odelling……Page 138
References……Page 147
6.1 Introduction……Page 149
6.2 Modeling Stock Market Prediction……Page 150
6.3 Intelligent Paradigms……Page 152
6.4 Ensemble of GP Techniques……Page 156
6.5 Experiment Results……Page 158
References……Page 162
7.1 Introduction……Page 165
7.2 Principles of Evolutionary Hardware Design……Page 166
7.3 Circuit Designs = Programs……Page 170
7.4 Circuit Designs = Schematics……Page 173
7.5 Result Comparison……Page 179
References……Page 189
8.1 Introducing Khepera Robot……Page 190
8.2 Evolving Complex Behaviors by Introducing Hierarchy to GP……Page 192
8.3 Evolving Complex Behaviors by Introducing Hierarchy to the Controller……Page 201
8.4 Comments……Page 206
8.5 Summary……Page 207
References……Page 208
Douglas Mota Dias, Marco Aurélio C. Pacheco and José F. M. Amaral……Page 209
9.1 Introduction……Page 210
9.2 Survey on Genetic Programming Applied to Synthesis of Assembly……Page 211
9.3 Design with Microcontrollers……Page 214
9.4 Microcontroller Platform……Page 216
9.5 Linear Genetic Programming……Page 217
9.6 System for Automatic Synthesis of Microcontroller Assembly……Page 218
9.7 Case Studies……Page 224
9.8 Summary……Page 241
References……Page 242
back-matter.pdf……Page 244