Numeric Mutation: Improved Search in Genetic Programming

Matthew Evett and Thomas Fernandez

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.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.