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Wise Breeding GA via Machine Learning Techniques for Function Optimization
Xavier Llorà and David E. Goldberg
Illinois Genetic Algorithms Laboratory (IlliGAL),
National Center for Supercomputing Applications,
University of Illinois at Urbana-Champaign,
104 S. Mathews Avenue,
Urbana, IL 61801.
{xllora,deg}@illigal.ge.uiuc.edu
Abstract. This paper explores how inductive machine learning can guide the breeding process of evolutionary algorithms for black-box function optimization. In particular, decision trees are used to identify the underlying characteristics of good and bad individuals, using the mined knowledge for wise breeding purposes. Inductive learning is complemented with statistical learning in order to define the breeding process. The proposed evolutionary process optimizes the fitness function in a dual manner, both maximizing and minimizing it. The paper also summarize some tuning and population sizing issues, as well as some preliminary results obtained using the proposed algorithm.
LNCS 2723, p. 1172 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003