Genetic Systems Programming: Theory and Experiences (Studies in Computational Intelligence, 13)

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. front-matter.pdf 1 001-020.pdf 20 1 Evolutionary Computation: from Genetic Algorithmsto Genetic Programming 20 Ajith Abraham, Nadia Nedjah and Luiza de Macedo Mourelle 20 1.1 Introduction 21 1.2 Genetic Algorithms 22 1.3 Evolution Strategies 28 1.4 Evolutionary Programming 30 1.5 Genetic Programming 31 1.6 Variants of Genetic Programming 34 1.7 Summary 38 References 38 021-056.pdf 40 2 Automatically Defined Functions in Gene Expression Programming 40 Cândida Ferreira 40 2.1 Genetic Algorithms: Historical Background 40 2.2 The Architecture of GEP Individuals 46 2.3 Chromosome Domains and Random Numerical Constants 52 2.4 Cells and the Creation of Automatically Defined Functions 55 2.5 Analyzing the Importance of ADFs in Automatic Programming 59 2.6 Summary 73 References 74 057-079.pdf 76 3 Evolving Intrusion Detection Systems 76 Ajith Abraham and Crina Grosan 76 3.1 Introduction 76 3.2 Intrusion Detection 77 3.3 Related Research 79 3.4 Evolving IDS Using Genetic Programming (GP) 82 3.5 Machine Learning Techniques 85 3.6 Experiment Setup and Results 87 3.7 Conclusions 96 References 96 081-104.pdf 99 4 Evolutionary Pattern Matching Using Genetic Programming 99 Nadia Nedjah and Luiza de Macedo Mourelle 99 4.1 Introduction 100 4.2 Preliminary Notation and Terminology 101 4.3 Adaptive Pattern Matching 104 4.4 Heuristics for Good Traversal Orders 108 4.5 Genetically-Programmed Matching Automata 110 4.6 Comparative Results 119 4.7 Summary 120 References 121 105-130.pdf 123 5 Genetic Programming in Data Modelling 123 Halina Kwasnicka and Ewa Szpunar-Huk 123 5.1 Introduction 123 5.2 Genetic Programming in Mathematical Modelling 124 5.3 Decision Models for Classification Tasks 132 5.4 GP for Prediction Task and Time Series Odelling 138 5.5 Summary 147 References 147 131-146.pdf 149 6 Stock Market Modeling Using Genetic Programming Ensembles 149 Crina Grosan and Ajith Abraham 149 6.1 Introduction 149 6.2 Modeling Stock Market Prediction 150 6.3 Intelligent Paradigms 152 6.4 Ensemble of GP Techniques 156 6.5 Experiment Results 158 6.6 Summary 162 References 162 147-171.pdf 165 7 Evolutionary Digital Circuit Design Using Genetic Programming 165 Nadia Nedjah and Luiza de Macedo Mourelle 165 7.1 Introduction 165 7.2 Principles of Evolutionary Hardware Design 166 7.3 Circuit Designs = Programs 170 7.4 Circuit Designs = Schematics 173 7.5 Result Comparison 179 7.6 Conclusion 189 References 189 173-191.pdf 190 8 Evolving Complex Robotic Behaviors Using Genetic Programming 190 Michael Botros 190 8.1 Introducing Khepera Robot 190 8.2 Evolving Complex Behaviors by Introducing Hierarchy to GP 192 8.3 Evolving Complex Behaviors by Introducing Hierarchy to the Controller 201 8.4 Comments 206 8.5 Summary 207 References 208 193-227.pdf 209 9 Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming 209 Douglas Mota Dias, Marco Aurélio C. Pacheco and José F. M. Amaral 209 9.1 Introduction 210 9.2 Survey on Genetic Programming Applied to Synthesis of Assembly 211 9.3 Design with Microcontrollers 214 9.4 Microcontroller Platform 216 9.5 Linear Genetic Programming 217 9.6 System for Automatic Synthesis of Microcontroller Assembly 218 9.7 Case Studies 224 9.8 Summary 241 References 242 back-matter.pdf 244 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