Numeric Mutation: Improved Search in Genetic Programming

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.