Genetic Programming Theory and Practice / Edition 1

Genetic Programming Theory and Practice / Edition 1

by Rick Riolo
     
 

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

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Overview

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. The book also includes chapters on the dynamics of GP, the selection of operators and population sizing, specific applications such as stock selection in emerging markets, predicting oil field production, modeling chemical production processes, and developing new diagnostics from genomic data. Genetic Programming Theory and Practice is an excellent reference for researchers working in evolutionary algorithms and for practitioners seeking innovative methods to solve difficult computing problems.

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Product Details

ISBN-13:
9781402075810
Publisher:
Springer US
Publication date:
11/30/2003
Series:
Genetic Programming Series, #6
Edition description:
2003
Pages:
336
Product dimensions:
9.21(w) x 6.14(h) x 0.75(d)

Related Subjects

Table of Contents

Contributing Authors
Preface
Foreword
1Genetic Programming: Theory and Practice1
2An Essay Concerning Human Understanding of Genetic Programming11
3Classification of Gene Expression Data with Genetic Programming25
4Artificial Regulatory Networks and Genetic Programming43
5Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms63
6Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming81
7What Makes a Problem GP-Hard?99
8A Probabilistic Model of Size Drift119
9Building-Block Supply in Genetic Programming137
10Modularization by Multi-Run Frequency Driven Subtree Encapsulation155
11The Distribution of Reversible Functions is Normal173
12Doing Genetic Algorithms the Genetic Programming Way189
13Probabilistic Model Building and Competent Genetic Programming205
14Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development221
15Industrial Strength Genetic Programming239
16Operator Choice and the Evolution of Robust Solutions257
17A Hybrid GP-Fuzzy Approach for Resevoir Characterization271
18Enhanced Emerging Market Stock Selection291
19Three Fundamentals of the Biological Genetic Algorithm303
Index313

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