Langdon W.B., Poli R. Foundations of Genetic Programming

Springer, 2002. — 265 p.Genetic programming (GP) has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how to do it. Since its inception more than ten years ago genetic programming has been used to solve practical problems but along with this engineering approach there has been interest in how and why it works. This book consolidates this theoretical work.
One of the goals of any theoretical work is to better understand the subject. This is useful in its own right and as an aid to designing improvements. We will describe several new genetic operators that arose naturally from theoretical work and suggest modest changes to the way existing GP systems could be used on specific problems to yield improved performance. No doubt these operators and suggestions will be of direct practical interest, even to those who are not interested in "theory" for its own sake.
Genetic programming is one of a wide range of evolutionary computation techniques, such as evolutionary strategies and evolutionary programming, being itself a descendent of one of the oldest, Genetic Algorithms (GAs). It is nice to be able to report in this book that theoretical results from the "new boy", GP, can be directly applied to GAs. Since GP is more expressive than GAs, it can be viewed as a generalisation of GAs. In the same way, GP theory is a generalisation of GA theory, although, in fact, some recent advances in GP theory came first and the corresponding GA theory was derived by specialising the more general GP theory. In effect we are getting GA theory for free, from the GP theory. In this way the various strands of evolutionary computation theory are themselves coming together (although convergence is some way off).Fitness Landscapes
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: Theoretical Analysis
Example I: The Artificial Ant
Example II: The Max Problem
GP Convergence and BloatA: Genetic Programming Resources