Genetic
programming (GP), one of the most advanced forms of
evolutionary computation, has been highly successful as a
technique for getting computers to automatically solve
problems without having to tell them explicitly how. Since
its inceptions more than ten years ago,
GP has been used to
solve practical problems in a variety of application
fields. Along with this ad-hoc engineering
approaches
interest increased in how and why GP works. This book
provides a coherent consolidation of recent work on the
theoretical foundations of GP. A concise introduction to GP
and
genetic algorithms (GA) is followed by a discussion of
fitness landscapes and other theoretical approaches to
natural and artificial evolution. Having surveyed early
approaches to GP theory it presents new exact
schema
analysis, showing that it applies to GP as well as to the
simpler GAs. New results on the potentially infinite number
of possible programs are followed by two chapters applying
these new techniques.
Contents
- Introduction
- Fitness Landscape
- Program Component Schema Theories
- Pessimistic GP Schema Theories
- Exact GP Schema Theorems
- Lessons from the GP Schema Theory
- The Genetic Programming Search Space
- The GP Search Space : Theorical Analysis
- Example I : The Artificial Ant
- Example II : The Max Problem
- GP Convergence and Bloat
- Conclusions
- Genetic Programming Resources
- Bibliography
- Glossary
- Index