Genetic Programming Theory and Practice IV

  • Rick Riolo
  • Terence Soule
  • Bill Worzel

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

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Terence Soule, Rick L. Riolo, Bill Worzel
    Pages 1-10
  3. W. P. Worzel, A. Almal, C. D. MacLean
    Pages 29-40
  4. Mark Kotanchek, Guido Smits, Ekaterina Vladislavleva
    Pages 167-185
  5. Tina Yu, Dave Wilkinson, Alexandre Castellini
    Pages 187-201
  6. Xiangdong Peng, Erik D. Goodman, Ronald C. Rosenberg
    Pages 203-217
  7. Jason M. Daida, Ricky Tang, Michael E. Samples, Matthew J. Byom
    Pages 237-256
  8. Riccardo Poli, William B. Langdon
    Pages 257-278
  9. Michael F. Korns
    Pages 299-314
  10. Ying L. Becker, Peng Fei, Anna M. Lester
    Pages 315-334
  11. Back Matter
    Pages 335-338

About this book


Genetic Programming Theory and Practice IV was developed from the fourth 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.

This valuable reference discusses the hurdles faced in solving large-scale, cutting edge applications, describes promising techniques, including fitness approximation, Pareto optimization, cooperative teams, solution caching, and experiment control, and investigates evolutionary approaches such as financial modeling, bioinformatics, symbolic regression for system modeling, and evolutionary design of circuits and robot controllers.

Genetic Programming Theory and Practice IV represents a watershed moment in the GP field in that GP has begun to move from hand-crafted software used primarily in academic research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.


Automat Boosting algorithm algorithms artificial intelligence classification complex system genetic algorithms learning machine learning modeling optimization programming robot stability

Editors and affiliations

  • Rick Riolo
    • 1
  • Terence Soule
    • 2
  • Bill Worzel
    • 3
  1. 1.Center for the Study of Complex SystemsUniversity of MichiganUSA
  2. 2.University of IdahoUSA
  3. 3.Genetics Squared, Inc.USA

Bibliographic information