Genetic programming is relatively poor at discovering useful numeric constants for the terminal nodes of its s-expression trees. In this paper we outline an adaptation to genetic programming, called numeric mutation. We provide empirical evidence and analysis that demonstrate that numeric mutation makes a statistically significant increase in genetic programming’s performance for symbolic regression problems.
Published Date: May 1998
Registration: ISBN 978-1-57735-051-4
Copyright: Published by The AAAI Press, Menlo Park, California