Genetic Algorithms: Principles and Perspectives

1. Front Matter

   Pages i-x

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2. Introduction

   Pages 1-18

3. Basic Principles

   Pages 19-63

4. Schema Theory

   Pages 65-93

5. No Free Lunch for GAs

   Pages 95-109

6. GAs as Markov Processes

   Pages 111-140

7. The Dynamical Systems Model

   Pages 141-171

8. Statistical Mechanics Approximations

   Pages 173-200

9. Predicting GA Performance

   Pages 201-230

10. Landscapes

    Pages 231-263

11. Summary

    Pages 265-285

12. Back Matter

    Pages 287-332

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Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.

• Stochastic Processes
• algorithms
• dynamical systems
• genetic algorithms
• linear algebra
• optimization

• School of Mathematical and Information Sciences, Coventry University, UK

  Colin R. Reeves

• School of Computer Science, University of Birmingham, USA

  Jonathan E. Rowe

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