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.

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