Enrique Alba, Carlos Cotta Departamento de Lenguajes y Ciencias de la Computación Universidad de Málaga, España

TABLE OF CONTENTS

I. INTRODUCTION TO NATURE-INSPIRED ALGORITHMIC TECHNIQUES

I.1 Introduction to Evolutionary Computation

I.2 Important Paradigms in Evolutionary Computation

I.3 Characterization of Evolutionary Computation

I.4 Common Components in EC Algorithms

I.5 A Distinguished EC Representative: Genetic Algorithms

I.6 Relationship with other Techniques

I.7 Important and Present Research Areas in Evolutionary Computation

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II. GENETIC ALGORITHMS

II.1 Basic Operations

II.2 Schema Theorem and Theoretical Background

II.3 Solving a Problem: Genotype and Fitness

II.4 Models of Evolution

II.5 Advanced Operators

II.6 Non-Conventional Genotypes

II.7 Important Issues in the Implementation of a GA

II.8 Response to Selection

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III. APPLICATIONS OF GENETIC ALGORITHMS

III.1 Index of Most Important Applications of the Genetic Algorithms

III.2 GAs for the Design of Neural Networks

III.3 GAs in Combinatorial Optimization

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IV. MODELS FOR THE PARALLELIZATION OF GENETIC ALGORITHMS

IV.1 Need of Parallelization

IV.2 Fine and Coarse Grain Models

IV.3 A Global Vision of the Existing Parallel GA Software

IV.4 Object Orientation and Parallelism

IV.5 Applications and Relationships with the Sequential Models

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V. DIRECTLY RELATED GA TECHNIQUES

V.1 Adaptive Parameterizations

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VI. GENETIC PROGRAMMING

VI.1 Background

VI.2 GP for the Generation of Fuzzy Rules

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VII. EC RELATED TECHNIQUES

VII.1 Tabu Search

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VIII. HYBRIDIZATION OF EC ALGORITHMS

VIII.1 GA + Branch and Bound

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APPENDIX A. BIBLIOGRAPHY OF APPLICATION-RELATED RESEARCH

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APPENDIX B. FIGURE INDEX

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APPENDIX C. SIMULATION OF A GA

		Enrique Alba Torres / eat@lcc.uma.es Carlos Cotta Porras / ccottap@lcc.uma.es
		Design & Elaboration: Enrique Alba Torres Carlos Cotta Porras Antonio J. Dorado Ruiz

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This page was updated on 27-Jul-2001