Context Matters: Adaptive Mutation for Grammars

Pedro Carvalho, Jessica Mégane, Nuno Lourenço, and Penousal Machado

Nominated for Best Paper of EuroGP

PDF Code .BIB

Abstract

This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure to enhance the benefits of the propose mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.

DOI

10.1007/978-3-031-29573-7_8

Recommended citation

Carvalho, P., Mégane, J., Lourenço, N., Machado, P. (2023). Context Matters: Adaptive Mutation for Grammars. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_8