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Genetic Programming Theory and Practice V

  • Book
  • © 2008

Overview

Editors:
  1. Rick Riolo

    1. Center for the Study of Complex Systems, University of Michigan, Ann Arbor

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    You can also search for this editor in PubMed   Google Scholar

  2. Terence Soule

    1. Department of Computer Science, University of Idaho, Moscow

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    You can also search for this editor in PubMed   Google Scholar

  3. Bill Worzel

    1. Genetics Squared, Ann Arbor

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    You can also search for this editor in PubMed   Google Scholar

  • Discusses hurdles in solving large-scale applications
  • Describes techniques including fitness and age layered populations, code reuse through caching, archives and libraries, Pareto optimization, pre- and post-processing, the use of expert knowledge and information-theoretic fitness measures
  • Addresses practical methods for choosing between techniques for improving GP performance and to evolve trustable solutions

Part of the book series: Genetic and Evolutionary Computation (GEVO)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Genetic Programming: Theory and Practice

    • Terence Soule, Rick L. Riolo, Bill Worzel
    Pages 1-12

  3. Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming

    • Ekaterina Vladislavleva, Guido Smits, Mark Kotanchek
    Pages 13-32

  4. Manipulation of Convergence in Evolutionary Systems

    • Gearoid Murphy, Conor Ryan
    Pages 33-52

  5. Large-Scale, Time-Constrained Symbolic Regression-Classification

    • Michael F. Korns
    Pages 53-68

  6. Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction

    • Jason H. Moore, Nate Barney, Bill C. White
    Pages 69-85

  7. Towards an Information Theoretic Framework for Genetic Programming

    • Stuart W. Card, Chilukuri K. Mohan
    Pages 87-106

  8. Investigating Problem Hardness of Real Life Applications

    • Leonardo Vanneschi
    Pages 107-124

  9. Improving the Scalability of Generative Representations for Openended Design

    • Gregory S. Hornby
    Pages 125-142

  10. Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming

    • A. A. Almal, C. D. MacLean, W. P. Worzel
    Pages 143-158

  11. Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design

    • Trent McConaghy, Pieter Palmers, Georges Gielen, Michiel Steyaert
    Pages 159-184

  12. Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs

    • Xiangdong Peng, Erik D. Goodman, Ronald C. Rosenberg
    Pages 185-200

  13. Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models

    • Mark Kotanchek, Guido Smits, Ekaterina Vladislavleva
    Pages 201-220

  14. Improving Performance and Cooperation in Multi-Agent Systems

    • Terence Soule, Robert B. Heckendorn
    Pages 221-237

  15. An Empirical Study of Multi-Objective Algorithms for Stock Ranking

    • Ying L. Becker, Harold Fox, Peng Fei
    Pages 239-259

  16. Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center

    • Robert G. Reynolds, Mostafa Z. Ali, Patrick Franzel
    Pages 261-276

  17. Back Matter

    Pages 277-279
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Keywords

About this book

Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.

Editors and Affiliations

  • Center for the Study of Complex Systems, University of Michigan, Ann Arbor

    Rick Riolo

  • Department of Computer Science, University of Idaho, Moscow

    Terence Soule

  • Genetics Squared, Ann Arbor

    Bill Worzel

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