Generality versus Size in Genetic Programming
Justinian P. Rosca · The MIT Press eBooks · 1996
Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game. 1 Introduction Genetic Programming (Koza, 1992) has been applied to a variety of machine learning applications formulated mostly as classification or prediction problems. Some examples include the prediction of omega loops in proteins and the transmembrane problem, symbolic regression...