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Computer Science - Security and Cryptology | Genetic Programming Theory and Practice

Genetic Programming Theory and Practice

Series: Genetic Programming, Vol. 6

Riolo, Rick, Worzel, Bill (Eds.)

2003, XVI, 317 p.

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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 Level » Research

Keywords » algorithms - circuit design - complex system - evolutionary algorithm - genetic programming - learning - machine learning - programming

Related subjects » Artificial Intelligence - Electronics & Electrical Engineering - Security and Cryptology - Theoretical Computer Science

Table of contents 

1 Genetic Programming: Theory and Practice.- 2 An Essay Concerning Human Understanding for Genetic Programming.- 3 Classification of Gene Expression Data with Genetic Programming.- 4 Artificial Regulatory Networks and Genetic Programming.- 5 Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms.- 6 Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming.- 7 What Makes a Problem GP-Hard?.- 8 A Probalistic Model of Size Drift.- 9 Building-Block Supply in Genetic Programming.- 10 Modularization by Multi-Run Frequency Driven Subtree Encapsulation.- 11 The Distribution of Reversible Functions is Normal.- 12 Doing Genetic Algorithms the Genetic Programming Way.- 13 Probalistic Model Building and Competent Genetic Programming.- 14 Automated Synthesis by Means of Genetic Programming Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development.- 15 Industrial Strength Genetic Programming.- 16 Operator Choice and the Evolution of Robust Solution.- 17 A Hybrid GP-Fuzzy Approach for Reservoir Characterization.- 18 Enhanced Emerging Market Stock Selection.- 19 Three Fundamentals of the Biological Genetic Algorithm.

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