Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution

Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution

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Product Details

ISBN-13: 9780262232432
Publisher: MIT Press
Publication date: 01/06/2006
Series: Vienna Series in Theoretical Biology
Pages: 400
Sales rank: 1,056,241
Product dimensions: 7.00(w) x 9.00(h) x 1.05(d)
Age Range: 18 Years

About the Author


Richard A. Watson is a Senior Lecturer in the BIO@ECS Research Group, School of Electronics and Computer Science, University of Southampton.


Gerd B. Müller, is Professor of Zoology and Head of the Department of Theoretical Biology at the University of Vienna and President of the Konrad Lorenz Institute for Evolution and Cognition Research.

Table of Contents

Prefacexi
Acknowledgmentsxvii
Series Forewordxix
1Introduction1
1.1Gradual and Compositional Evolution3
1.2The Algorithmic Paradigms of Evolution8
1.3Complex Systems with Modular Interdependency and Their (Un)evolvability12
1.4Compositional Mechanisms16
1.5The Impact on Gradualism19
1.6Some Related Issues24
1.7Contributions27
2Gradual Evolution29
2.1The Gradualist Framework of Evolution29
2.2Evolutionary Algorithms31
2.3Concepts of Evolutionary Difficulty35
2.4Summary43
3Compositional Evolution45
3.1Compositional Mechanisms45
3.2Models of Composition55
3.3Some Issues in the Use of Compositional Mechanisms81
3.4Some Conceptual Issues of Compositional Evolution90
3.5Summary97
4Modularity101
4.1Interdependency101
4.2Modular Interdependency109
4.3Hierarchical Modular Interdependency117
4.4Hierarchical-If-and-Only-If (HIFF)125
4.5Discussion129
4.6Summary146
5Mutation149
5.1Examining the Fitness Landscape149
5.2Difficulty of Modular Interdependency for Gradual Mechanisms150
5.3Expected Time to Solution for Gradual Mechanisms155
5.4Simulation Results for Mutation157
5.5Summary160
6Sexual Recombination163
6.1Overview of Models164
6.2Results for a Single Panmictic Population-The Simple GA165
6.3Results for a Subdivided or Niched Population-GA with Crowding167
6.4The Dependence on Physical Linkage174
6.5The Impact for GA Theory183
6.6Summary185
7Symbiotic Encapsulation191
7.1An Overview of the Symbiotic Encapsulation Model191
7.2Entities and Their Encapsulation193
7.3Evaluation and Selection200
7.4The Symbiogenic Evolutionary Adaptation Model (SEAM)205
7.5Simulation Results for Symbiotic Encapsulation205
7.6The Relationship of SEAM to Other Algorithmic Methods210
7.7Summary217
8How Fast Is Fast?219
8.1An Analysis of Sexual Recombination on HIFF219
8.2An Analysis of SEAM on Shuffled HIFF234
8.3Summary243
9Scaling Up Evolution245
9.1Units in Sexual Populations247
9.2Scaling Up Evolution with Symbiotic Encapsulation255
9.3The Inherent Tension of Innovation and Reproductive Fidelity265
10The Impact of Compositional Evolution267
10.1Future and Ongoing Research267
10.2Large Directed Adaptive Genetic Changes274
10.3Symbiosis as a Source of Evolutionary Innovation276
10.4Evolutionary Difficulty and Gradualism277
10.5Algorithmic Principles of Adaptation277
10.6The Availability and Impact of Compositional Mechanisms in Nature279
10.7Modularity in Natural Systems280
10.8Conclusions285
Notes291
Glossary295
References303
Index319

What People are Saying About This

Melanie Mitchell

This book is a work of truly interdisciplinary science; it demonstrates that the joint study of evolutionary computation and evolutionary biology can produce important results for both fields. It is also a wonderful case study in the effectiveness of abstract computer modeling of biological ideas. Watson's book is essential reading for computer scientists who look to biology for problem-solving methods, and for evolutionary biologists who want to know how ideas from computation can create new perspectives on their field.

Endorsement

This book is a work of truly interdisciplinary science; it demonstrates that the joint study of evolutionary computation and evolutionary biology can produce important results for both fields. It is also a wonderful case study in the effectiveness of abstract computer modeling of biological ideas. Watson's book is essential reading for computer scientists who look to biology for problem-solving methods, and for evolutionary biologists who want to know how ideas from computation can create new perspectives on their field.

Melanie Mitchell, Department of Computer Science, Portland State University, author of An Introduction to Genetic Algorithms

From the Publisher

"This book is a work of truly interdisciplinary science; it demonstrates that the joint study of evolutionary computation and evolutionary biology can produce important results for both fields. It is also a wonderful case study in the effectiveness of abstract computer modeling of biological ideas. Watson's book is essential reading for computer scientists who look to biology for problem-solving methods, and for evolutionary biologists who want to know how ideas from computation can create new perspectives on their field."—Melanie Mitchell, Department of Computer Science, Portland State University, author of *An Introduction to Genetic Algorithms*

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