Genetic Programming Theory and Practice II

  • Una-May O’Reilly
  • Tina Yu
  • Rick Riolo
  • Bill Worzel

Part of the Genetic Programming book series (GPEM, volume 8)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Una-May O’Reilly, Tina Yu, Rick Riolo, Bill Worzel
    Pages 1-10
  3. Flor Castillo, Arthur Kordon, Jeff Sweeney, Wayne Zirk
    Pages 31-48
  4. Kumara Sastry, Una-May O’Reilly, David E. Goldberg
    Pages 49-65
  5. Conor Ryan, Maarten Keijzer, Mike Cattolico
    Pages 103-120
  6. John R. Koza, Lee W. Jones, Martin A. Keane, Matthew J. Streeter, Sameer H. Al-Sakran
    Pages 121-142
  7. W. Banzhaf, C. Lasarczyk
    Pages 175-190
  8. Cezary Z. Janikow
    Pages 191-206
  9. A. Beatriz Garmendia-Doval, Julian F. Miller, S. David Morley
    Pages 225-244
  10. Duncan MacLean, Eric A. Wollesen, Bill Worzel
    Pages 245-262
  11. Daniel Howard, Simon C. Roberts
    Pages 263-282
  12. Guido F. Smits, Mark Kotanchek
    Pages 283-299
  13. Jason D. Lohn, Gregory S. Hornby, Derek S. Linden
    Pages 301-315
  14. Back Matter
    Pages 317-320

About this book

Introduction

This volume explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The contributions developed from a second workshop at the University of Michigan's Center for the Study of Complex Systems where leading international genetic programming theorists from major universities and active practitioners from leading industries and businesses met to examine how GP theory informs practice and how GP practice impacts GP theory. Chapters include such topics as financial trading rules, industrial statistical model building, population sizing, the roles of structure in problem solving by computer, stock picking, automated design of industrial-strength analog circuits, topological synthesis of robust systems, algorithmic chemistry, supply chain reordering policies, post docking filtering, an evolved antenna for a NASA mission and incident detection on highways.

Keywords

Algorithms Automat algorithm computer genetic programming learning machine learning programming

Editors and affiliations

  • Una-May O’Reilly
    • 1
  • Tina Yu
    • 2
  • Rick Riolo
    • 3
  • Bill Worzel
    • 4
  1. 1.Massachusetts Institute of TechnologyUSA
  2. 2.Chevron Texaco Information Technology GroupChevron
  3. 3.University of Michigan
  4. 4.Genetics Squared, Inc.Genetics

Bibliographic information

  • DOI https://doi.org/10.1007/b101112
  • Copyright Information Springer Science+Business Media, Inc. 2005
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-23253-9
  • Online ISBN 978-0-387-23254-6
  • Series Print ISSN 1566-7863
  • About this book