Wise Breeding GA via Machine Learning Techniques for Function Optimization
TR No.: 2003006 | Download PDF | Download PS
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 target function dually, maximizing and minimizing it at the same time. The paper also summarizes some tuning and population sizing issues, as well as some preliminary results obtained using the proposed algorithm.
Posted: January 24th, 2003 under Genetic algorithms.
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