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Genetic Algorithms and Genetic Programming

Modern Concepts and Practical Applications

By: Andreas Beham, Stephan Winkler, Stefan Wagner, Michael Affenzeller

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Hardcover | 9 April 2009 | Edition Number 1

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Hardcover
394 Pages

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24.1 x 16.6 x 2.5

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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.

The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.

Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

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You Can Find This Book In

Non-Fiction Science Biology, Life Sciences Life Sciences in General Genetics excluding Medical Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Artificial Intelligence Machine Learning Mathematics

Databases Data Mining Digital Lifestyle & Online World: Consumer & User Guides Computer Games & Online Games Strategy Guides

List of Tables	p. xi
List of Figures	p. xv
List of Algorithms	p. xxiii
Introduction	p. xxv
Simulating Evolution: Basics about Genetic Algorithms	p. 1
The Evolution of Evolutionary Computation	p. 1
The Basics of Genetic Algorithms	p. 2
Biological Terminology	p. 3
Genetic Operators	p. 6
Models for Parent Selection	p. 6
Recombination (Crossover)	p. 7
Mutation	p. 9
Replacement Schemes	p. 9
Problem Representation	p. 10
Binary Representation	p. 11
Adjacency Representation	p. 12
Path Representation	p. 13
Other Representations for Combinatorial Optimization Problems	p. 13
Problem Representations for Real-Valued Encoding	p. 14
GA Theory: Schemata and Building Blocks	p. 14
Parallel Genetic Algorithms	p. 17
Global Parallelization	p. 18
Coarse-Grained Parallel GAs	p. 19
Fine-Grained Parallel GAs	p. 20
Migration	p. 21
The Interplay of Genetic Operators	p. 22
Bibliographic Remarks	p. 23
Evolving Programs: Genetic Programming	p. 25
Introduction: Main Ideas and Historical Background	p. 26
Chromosome Representation	p. 28
Hierarchical Labeled Structure Trees	p. 28
Automatically Defined Functions and Modular Genetic Programming	p. 35
Other Representations	p. 36
Basic Steps of the GP-Based Problem Solving Process	p. 37
Preparatory Steps	p. 37
Initialization	p. 39
Breeding Populations of Programs	p. 39
Process Termination and Results Designation	p. 41
Typical Applications of Genetic Programming	p. 43
Automated Learning of Multiplexer Functions	p. 43
The Artificial Ant	p. 44
Symbolic Regression	p. 46
Other GP Applications	p. 49
GP Schema Theories	p. 50
Program Component GP Schemata	p. 51
Rooted Tree GP Schema Theories	p. 52
Exact GP Schema Theory	p. 54
Summary	p. 59
Current GP Challenges and Research Areas	p. 59
Conclusion	p. 62
Bibliographic Remarks	p. 62
Problems and Success Factors	p. 65
What Makes GAs and GP Unique among Intelligent Optimization Methods?	p. 65
Stagnation and Premature Convergence	p. 66
Preservation of Revelant Building Blocks	p. 69
What Can Extended Selection Concepts Do to Avoid Premature Convergence?	p. 69
Offspring Selection (OS)	p. 70
The Revelant Alleles Preserving Genetic Algorithm (RAPGA)	p. 73
Consequences Arising out of Offspring Selection and RAPGA	p. 76
Sasegasa-More than the Sum of All Parts	p. 79
The Interplay of Distributed Search and Systematic Recovery of Essential Genetic Information	p. 80
Migration Revisited	p. 81
Sasegasa: A Novel and Self-Adaptive Parallel Genetic Algorithm	p. 82
The Core Algorithm	p. 83
Interactions among Genetic Drift, Migration, and Self-Adaptive Selection Pressure	p. 86
Analysis of Population Dynamics	p. 89
Parent Analysis	p. 89
Genetic Diversity	p. 90
In Single-Population GAs	p. 90
In Multi-Population GAs	p. 91
Application Examples	p. 92
Characteristics of Offspring Selection and the RAPGA	p. 97
Introduction	p. 97
Building Block Analysis for Standard GAs	p. 98
Building Block Analysis for GAs Using Offspring Selection	p. 103
Building Block Analysis for the Relevant Alleles Preserving GA (RAPGA)	p. 113
Combinatorial Optimization: Route Planning	p. 121
The Traveling Salesman Problem	p. 121
Problem Statement and Solution Methodology	p. 122
Review of Approximation Algorithms and Heuristics	p. 125
Multiple Traveling Salesman Problems	p. 130
Genetic Algorithm Approaches	p. 130
The Capacitated Vehicle Routing Problem	p. 139
Problem Statement and Solution Methodology	p. 140
Genetic Algorithm Approaches	p. 147
Evolutionary System Identification	p. 157
Data-Based Modeling and System Identification	p. 157
Basics	p. 157
An Example	p. 159
The Basic Steps in System Identification	p. 166
Data-Based Modeling Using Genetic Programming	p. 169
GP-Based System Identification in HeuristicLab	p. 170
Introduction	p. 170
Problem Representation	p. 171
The Functions and Terminals Basis	p. 173
Solution Representation	p. 178
Solution Evaluation	p. 182
Local Adaption Embedded in Global Optimization	p. 188
Parameter Optimization	p. 189
Pruning	p. 192
Similarity Measures for Solution Candidates	p. 197
Evaluation-Based Similarity Measures	p. 199
Structural Similarity Measures	p. 201
Applications of Genetic Algorithms: Combinatorial Optimization	p. 207
The Traveling Salesman Problem	p. 208
Performance Increase of Results of Different Crossover Operators by Means of Offspring Selection	p. 208
Scalability of Global Solution Quality by SASEGASA	p. 210
Comparison of the SASEGASA to the Island-Model Coarse-Grained Parallel GA	p. 214
Genetic Diversity Analysis for the Different GA Types	p. 217
Capacitated Vehicle Routing	p. 221
Results Achieved Using Standard Genetic Algorithms	p. 222
Results Achieved Using Standard Genetic Algorithms with Offspring Selection	p. 226
Data-Based Modeling with Genetic Programming	p. 235
Time Series Analysis	p. 235
Time Series Specific Evaluation	p. 236
Application Example: Design of Virtual Sensors for Emissions of Diesel Engines	p. 237
Classification	p. 251
Introduction	p. 251
Real-Valued Classification with Genetic Programming	p. 251
Analyzing Classifiers	p. 252
Classification Specific Evaluation in GP	p. 258
Application Example: Medical Data Analysis	p. 263
Genetic Propagation	p. 285
Test Setup	p. 285
Test Results	p. 286
Summary	p. 288
Additional Tests Using Random Parent Selection	p. 289
Single Population Diversity Analysis	p. 292
GP Test Strategies	p. 292
Test Results	p. 293
Conclusion	p. 297
Multi-Population Diversity Analysis	p. 300
GP Test Strategies	p. 300
Test Results	p. 301
Discussion	p. 303
Code Bloat, Pruning, and Population Diversity	p. 306
Introduction	p. 306
Test Strategies	p. 307
Test Results	p. 309
Conclusion	p. 318
Conclusion and Outlook	p. 321
Symbols and Abbreviations	p. 325
References	p. 327
Index	p. 359
Table of Contents provided by Ingram. All Rights Reserved.

Genetic Algorithms and Genetic Programming

Modern Concepts and Practical Applications

At a Glance

Hardcover

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