Genetic Programming Theory and Practice V

By Rick Riolo , Terence Soule , Bill Worzel

Genetic Programming Theory and Practice V Cover Image

  • ISBN13: 978-0-3877-6307-1
  • 293 Pages
  • User Level: Science
  • Publication Date: December 20, 2007
  • Available eBook Formats: PDF
  • eBook Price: $49.95
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Full Description
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.
Table of Contents

Table of Contents

  1. Contributing Authors.
  2. Preface.
  3. Foreword.
  4. Genetic Programming: Theory and Practice.
  5. Better Solutions Faster: Soft Evolution of Robust Regression Models in Pareto Genetic Programming.
  6. Manipulation of Convergence in Evolutionary Systems.
  7. Large
  8. Scale, Time
  9. Constrained Symbolic Regression
  10. Classification.
  11. Solving Complex Problems in Human Genetics Using Genetic Programming.
  12. Towards an Information Theoretic Framework for Genetic Programming.
  13. Investigating Problem Hardness in Real Life Applications.
  14. Improving the Scalability of Generative Representations for Open
  15. Ended Design.
  16. Program Structure
  17. Fitness Disconnect and Its Impact on Evolution in GP.
  18. Genetic Programming with Reuse of Known Designs.
  19. Robust Engineering Design of Electronic Circuits with Active Components Using Genetic Programming and Bond Graphs.
  20. Trustable Symbolic Regression Models.
  21. Improving Performance and Cooperation in Multi
  22. Agent Systems.
  23. An Empirical Study of Multi
  24. Objective Algorithms for Stock Ranking.
  25. Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center.
  26. Index.
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