Extending the Scalability of Linkage Learning Genetic Algorithms

Theory & Practice

By: Ying-ping Chen

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Hardcover | 6 October 2005

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146 Pages

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Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, most GAs employed in practice nowadays are unable to learn genetic linkage and suffer from the linkage problem. The linkage learning genetic algorithm (LLGA) was proposed to tackle the linkage problem with several specially designed mechanisms. While the LLGA performs much better on badly scaled problems than simple GAs, it does not work well on uniformly scaled problems as other competent GAs. Therefore, we need to understand why it is so and need to know how to design a better LLGA or whether there are certain limits of such a linkage learning process. This book aims to gain better understanding of the LLGA in theory and to improve the LLGA's performance in practice. It starts with a survey of the existing genetic linkage learning techniques and describes the steps and approaches taken to tackle the research topics, including using promoters, developing the convergence time model, and adopting subchromosomes.

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List of Figures	p. XV
List of Tables	p. XVII
List of Abbreviations	p. XIX
Introduction	p. 1
Research Objectives	p. 2
Road Map	p. 3
Genetic Algorithms and Genetic Linkage	p. 5
Overview of Genetic Algorithms	p. 6
Representation, Fitness, and Population	p. 6
Selection, Crossover, and Mutation	p. 7
Goldberg's Design Decomposition	p. 9
Population-Sizing Model	p. 11
Competent Genetic Algorithms	p. 16
Genetic Linkage and the Linkage Problem	p. 17
What Is Genetic Linkage?	p. 17
Linkage Learning as an Ordering Problem	p. 20
Why Is Genetic Linkage Learning Important?	p. 20
Summary	p. 21
Genetic Linkage Learning Techniques	p. 23
Unimetric Approach vs. Multimetric Approach	p. 24
Physical Linkage vs. Virtual Linkage	p. 26
Distributed Model vs. Centralized Model	p. 28
LLGA: Precursors and Ancestors	p. 30
LLGA: Unimetric, Physical Linkage, and Distributed Model	p. 32
Summary	p. 33
Linkage Learning Genetic Algorithm	p. 35
Chromosome Representation	p. 35
Exchange Crossover	p. 38
Linkage Definition and Two Linkage Learning Mechanisms	p. 38
Quantifying Linkage	p. 39
Linkage Skew	p. 40
Linkage Shift	p. 40
Accomplishments of the LLGA	p. 41
Difficulties Faced by the LLGA	p. 42
Summary	p. 43
Preliminaries: Assumptions and the Test Problem	p. 45
Assumptions	p. 45
Test Problem	p. 47
Summary	p. 49
A First Improvement: Using Promoters	p. 51
A Critique of the Original LLGA	p. 52
Test Function	p. 52
What Is the LLGA Supposed to Do?	p. 52
How Does the LLGA Fail?	p. 53
Separation Inadequacy: Key Deficiency of the LLGA	p. 54
Improve Nucleation Potential with Promoters	p. 56
How Do Promoters Work?	p. 56
Modified Exchange Crossover	p. 57
Effect of the Modifications	p. 59
Summary	p. 61
Convergence Time for the Linkage Learning Genetic Algorithm	p. 63
Experimental Settings	p. 64
Sequentiality for Exponentially Scaled BBs	p. 65
Time to Convergence	p. 65
Building-Block Propagation	p. 66
Time to Tighten the First Building Block	p. 67
Sequentiality for Uniformly Scaled BBs	p. 67
Time to Convergence	p. 67
Building-Block Propagation	p. 69
Time to Tighten the First Building Block	p. 72
Macro View: Sequential Behavior	p. 72
Extending Linkage Learning Mechanisms	p. 74
Extending the Linkage-Skew Model	p. 76
Extending the Linkage-Shift Model	p. 78
Micro View: Tightness Time	p. 83
From One Building Block to m Building Blocks	p. 84
Genetic Material from the Donor	p. 86
Genetic Material on the Recipient	p. 86
Tightness Time for m Uniformly Scaled Building Blocks	p. 86
Convergence Time Model for the LLGA	p. 87
Summary	p. 90
Introducing Subchromosome Representations	p. 91
Limit to Competence of the LLGA	p. 91
Subchromosome Representations	p. 92
Chromosome Representation	p. 92
Exchange Crossover	p. 94
Empirical Verification	p. 94
Experimental Settings	p. 95
Experimental Results	p. 96
Summary	p. 98
Conclusions	p. 101
Summary	p. 101
Future Work	p. 103
Main Conclusions	p. 106
References	p. 109
Index	p. 117
Table of Contents provided by Publisher. All Rights Reserved.

Extending the Scalability of Linkage Learning Genetic Algorithms

Theory & Practice

At a Glance

Hardcover

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How to return your order

Defective items

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