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.

翻译：本文提出一种基于促进变异理论的自适应突变方法——自适应促进突变，专为结构化语法演化（Structured Grammatical Evolution，SGE）设计，其生物学灵感来源于促进变异理论。在SGE中，个体的基因型包含针对语法中每个非终结符的列表，这些非终结符定义了搜索空间。在我们提出的突变中，每个个体包含一个数组，其中为每个非终结符分配了不同的、自适应的突变率。同时，我们提出了一种语法设计流程——功能分组语法（Function Grouped Grammars），以增强所提突变方法的优势。实验采用概率结构化语法演化（Probabilistic Structured Grammatical Evolution，PSGE，SGE的变体）在三个符号回归基准上进行。结果表明，与标准语法和突变方法相比，我们的方法表现相当或更优。