Genetic Programming Theory and Practice VI

  • Bill Worzel
  • Terence Soule
  • Rick Riolo

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

Table of contents

  1. Front Matter
    Pages 1-14
  2. Terence Soule, Rick L. Riolo, Una-May O’Reilly
    Pages 1-18
  3. A. A. Almal, C. D. MacLean, W. P Worzel
    Pages 1-10
  4. Riccardo Poli, Nicholas F. McPhee, Leonardo Vanneschi
    Pages 1-20
  5. Trent McConaghy, Pieter Palmers, Georges Gielen, Michiel Steyaert
    Pages 1-14
  6. Mark Kotanchek, Guido Smits, Ekaterina Vladislavleva
    Pages 1-18
  7. Minkyu Kim, Ying L. Becker, Peng Fei, Una-May O’Reilly
    Pages 1-16
  8. Michael F. Korns, Loryfel Nunez
    Pages 1-14
  9. Wolfgang Banzhaf, Simon Harding, William B. Langdon, Garnett Wilson
    Pages 1-19
  10. Mostafa Z. Ali, Robert G. Reynolds, Xiangdong Che
    Pages 1-19
  11. Back Matter
    Pages 1-3

About this book


Genetic Programming Theory and Practice VI was developed from the sixth 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.

These contributions address several significant inter-dependent themes which emerged from this year's workshop, including:

  • Making efficient and effective use of test data
  • Sustaining the long term evolvability of our GP systems
  • Exploiting discovered subsolutions for reuse
  • Increasing the role of a Domain Expert

In the course of investigating these themes, the chapters describe a variety of techniques in widespread use among practitioners who deal with industrial-scale, real-world problems, such as:

  • Pareto optimization, particularly as a means to limit solution complexity

  • Various types of age-layered populations or niching mechanisms
  • Data partitioning, a priori or adaptively, e.g., via co-evolution
  • Cluster computing or general purpose graphics processors for parallel computing
  • Ensemble/team solutions

This work covers applications of GP to a host of domains, including bioinformatics, symbolic regression for system modeling in various settings, circuit design, and financial modeling to support portfolio management.

This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.


Artificial Intelligence Automatic Programming Computer Science Evolution of Models Evolutionary Computation Genetic Algorithms Problem Solving Search Algorithms Symbolic Regression algorithms classification complexity evolution genetic programming knowledge

Editors and affiliations

  • Bill Worzel
  • Terence Soule
  • Rick Riolo

There are no affiliations available

Bibliographic information