Ebook: Genetic Programming Theory and Practice

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

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Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

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Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

Content:
Front Matter....Pages i-xvi
Genetic Programming: Theory and Practice....Pages 1-10
An Essay Concerning Human Understanding of Genetic Programming....Pages 11-23
Classification of Gene Expression Data with Genetic Programming....Pages 25-42
Artificial Regulatory Networks and Genetic Programming....Pages 43-61
Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms....Pages 63-80
Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming....Pages 81-98
What Makes a Problem GP-Hard?....Pages 99-118
A Probabilistic Model of Size Drift....Pages 119-135
Building-Block Supply in Genetic Programming....Pages 137-154
Modularization by Multi-Run Frequency Driven Subtree Encapsulation....Pages 155-171
The Distribution of Reversible Functions is Normal....Pages 173-187
Doing Genetic Algorithms the Genetic Programming Way....Pages 189-204
Probabilistic Model Building and Competent Genetic Programming....Pages 205-220
Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development....Pages 221-237
Industrial Strength Genetic Programming....Pages 239-255
Operator Choice and the Evolution of Robust Solutions....Pages 257-269
A Hybrid GP-Fuzzy Approach for Resevoir Characterization....Pages 271-289
Enhanced Emerging Market Stock Selection....Pages 291-302
Three Fundamentals of the Biological Genetic Algorithm....Pages 303-311
Back Matter....Pages 313-317

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Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

Content:
Front Matter....Pages i-xvi
Genetic Programming: Theory and Practice....Pages 1-10
An Essay Concerning Human Understanding of Genetic Programming....Pages 11-23
Classification of Gene Expression Data with Genetic Programming....Pages 25-42
Artificial Regulatory Networks and Genetic Programming....Pages 43-61
Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms....Pages 63-80
Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming....Pages 81-98
What Makes a Problem GP-Hard?....Pages 99-118
A Probabilistic Model of Size Drift....Pages 119-135
Building-Block Supply in Genetic Programming....Pages 137-154
Modularization by Multi-Run Frequency Driven Subtree Encapsulation....Pages 155-171
The Distribution of Reversible Functions is Normal....Pages 173-187
Doing Genetic Algorithms the Genetic Programming Way....Pages 189-204
Probabilistic Model Building and Competent Genetic Programming....Pages 205-220
Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development....Pages 221-237
Industrial Strength Genetic Programming....Pages 239-255
Operator Choice and the Evolution of Robust Solutions....Pages 257-269
A Hybrid GP-Fuzzy Approach for Resevoir Characterization....Pages 271-289
Enhanced Emerging Market Stock Selection....Pages 291-302
Three Fundamentals of the Biological Genetic Algorithm....Pages 303-311
Back Matter....Pages 313-317
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