Ebook: Theoretical Aspects of Evolutionary Computing

During the first week of September 1999, the Second EvoNet Summer School on Theoretical Aspects of Evolutionary Computing was held at the Middelheim cam­ pus of the University of Antwerp, Belgium. Originally intended as a small get­ together of PhD students interested in the theory of evolutionary computing, the summer school grew to become a successful combination of a four-day workshop with over twenty researchers in the field and a two-day lecture series open to a wider audience. This book is based on the lectures and workshop contributions of this summer school. Its first part consists of tutorial papers which introduce the reader to a num­ ber of important directions in the theory of evolutionary computing. The tutorials are at graduate level andassume only a basic backgroundin mathematics and com­ puter science. No prior knowledge ofevolutionary computing or its theory is nec­ essary. The second part of the book consists of technical papers, selected from the workshop contributions. A number of them build on the material of the tutorials, exploring the theory to research level. Other technical papers may require a visit to the library.

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This book is the first in the field to provide extensive, entry level tutorials to the theory of Evolutionary Computing, covering the main approaches to understanding the dynamics of Evolutionary Algorithms. It combines this with recent, previously unpublished research papers based on the material of the tutorials. The outcome is a book which is self-contained to a large degree, attractive both to graduate students and researchers from other fields who want to get acquainted with the theory of Evolutionary Computing, and to active researchers in the field who can use this book as a reference and a source of recent results.

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This book is the first in the field to provide extensive, entry level tutorials to the theory of Evolutionary Computing, covering the main approaches to understanding the dynamics of Evolutionary Algorithms. It combines this with recent, previously unpublished research papers based on the material of the tutorials. The outcome is a book which is self-contained to a large degree, attractive both to graduate students and researchers from other fields who want to get acquainted with the theory of Evolutionary Computing, and to active researchers in the field who can use this book as a reference and a source of recent results.
Content:
Front Matter....Pages I-X
An Introduction to Evolutionary Computing in Design Search and Optimisation....Pages 1-11
Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions....Pages 13-30
The Dynamical Systems Model of the Simple Genetic Algorithm....Pages 31-57
Modelling Genetic Algorithm Dynamics....Pages 59-85
Statistical Mechanics Theory of Genetic Algorithms....Pages 87-108
Theory of Evolution Strategies — A Tutorial....Pages 109-133
Evolutionary Algorithms: From Recombination to Search Distributions....Pages 135-173
Properties of Fitness Functions and Search Landscapes....Pages 175-206
A Solvable Model of a Hard Optimisation Problem....Pages 207-221
Bimodal Performance Profile of Evolutionary Search and the Effects of Crossover....Pages 223-237
Evolution Strategies in Noisy Environments — A Survey of Existing Work....Pages 239-249
Cyclic Attractors and Quasispecies Adaptability....Pages 251-259
Genetic Algorithms in Time-Dependent Environments....Pages 261-285
Statistical Machine Learning and Combinatorial Optimization....Pages 287-306
Multi-Parent Scanning Crossover and Genetic Drift....Pages 307-330
Harmonic Recombination for Evolutionary Computation....Pages 331-342
How to Detect all Maxima of a Function....Pages 343-370
On Classifications of Fitness Functions....Pages 371-385
Genetic Search on Highly Symmetric Solution Spaces: Preliminary Results....Pages 387-407
Structure Optimization and Isomorphisms....Pages 409-422
Detecting Spin-Flip Symmetry in Optimization Problems....Pages 423-437
Asymptotic Results for Genetic Algorithms with Applications to Nonlinear Estimation....Pages 439-493
Back Matter....Pages 495-499

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This book is the first in the field to provide extensive, entry level tutorials to the theory of Evolutionary Computing, covering the main approaches to understanding the dynamics of Evolutionary Algorithms. It combines this with recent, previously unpublished research papers based on the material of the tutorials. The outcome is a book which is self-contained to a large degree, attractive both to graduate students and researchers from other fields who want to get acquainted with the theory of Evolutionary Computing, and to active researchers in the field who can use this book as a reference and a source of recent results.
Content:
Front Matter....Pages I-X
An Introduction to Evolutionary Computing in Design Search and Optimisation....Pages 1-11
Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions....Pages 13-30
The Dynamical Systems Model of the Simple Genetic Algorithm....Pages 31-57
Modelling Genetic Algorithm Dynamics....Pages 59-85
Statistical Mechanics Theory of Genetic Algorithms....Pages 87-108
Theory of Evolution Strategies — A Tutorial....Pages 109-133
Evolutionary Algorithms: From Recombination to Search Distributions....Pages 135-173
Properties of Fitness Functions and Search Landscapes....Pages 175-206
A Solvable Model of a Hard Optimisation Problem....Pages 207-221
Bimodal Performance Profile of Evolutionary Search and the Effects of Crossover....Pages 223-237
Evolution Strategies in Noisy Environments — A Survey of Existing Work....Pages 239-249
Cyclic Attractors and Quasispecies Adaptability....Pages 251-259
Genetic Algorithms in Time-Dependent Environments....Pages 261-285
Statistical Machine Learning and Combinatorial Optimization....Pages 287-306
Multi-Parent Scanning Crossover and Genetic Drift....Pages 307-330
Harmonic Recombination for Evolutionary Computation....Pages 331-342
How to Detect all Maxima of a Function....Pages 343-370
On Classifications of Fitness Functions....Pages 371-385
Genetic Search on Highly Symmetric Solution Spaces: Preliminary Results....Pages 387-407
Structure Optimization and Isomorphisms....Pages 409-422
Detecting Spin-Flip Symmetry in Optimization Problems....Pages 423-437
Asymptotic Results for Genetic Algorithms with Applications to Nonlinear Estimation....Pages 439-493
Back Matter....Pages 495-499
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