Advances in Genetic Programming

Volume 1
Overview

There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.

Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail.

A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.

Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.

Table of Contents

  1. Contributors
  2. Preface
  3. Acknowledgments
  4. I. Introduction
  5. 1. A Perspective on the Work in this Book
  6. 2. Introduction to Genetic Programming
  7. II. Increasing the Power of Genetic Programming
  8. 3. The Evolution of Evolvability in Genetic Programming
  9. 4. Genetic Programming and Emergent Intelligence
  10. 5. Scalable Learning in Genetic Programming using Automatic Function Definition
  11. 6. Alternatives in Automatic Function Definition: A Comparison of Performance
  12. 7. The Donut Problem: Scalability Generalization and Breeding Policies in Genetic Programming
  13. 8. Effects of Locality in Individual and Population Evolution
  14. 9. The Evolution of Mental Models
  15. 10. Evolution of Obstacle Avoidance Behavior: Using Noise to Promote Robust Solutions
  16. 11. Pygmies and Civil Servants
  17. 12. Genetic Programming Using a Minimum Description Length Principle
  18. 13. Genetic Programming in C++: Implementation Issues
  19. 14. A Compiling Genetic Programming System that Directly Manipulates the Machine Code
  20. III. Innovative Applications of Genetic Programming
  21. 15. Automatic Generation of Programs for Crawling and Walking
  22. 16. Genetic Programming for the Acquisition of Double Auction Market Strategies
  23. 17. Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data
  24. 18. The Automatic Generation of Plans for a Mobile Robot via Genetic Programming with Automatically Defined Functions
  25. 19. Competitively Evolving Decision Trees Against Fixed Training Cases for Natural Language Processing
  26. 20. Cracking and Co-Evolving Randomizers
  27. 21. Optimizing Confidence of Text Classification by Evolution of Symbolic Expressions
  28. 22. Evolvable 3D Modeling for Model-Based Object Recognition Systems
  29. 23. Automatically Defined Features: The Simultaneous Evolutions of 2-Dimensional Feature Detectors and an Algorithm for Using Them
  30. 23. Genetic Micro Programming of Neural Networks
  31. Author Index