  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
proposed 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.
