The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analyzed.

翻译：在基于语法的遗传方案制定方法中使用的语法对所产生解决办法的质量有重大影响,因为它们通过限制其语法的解决方案来界定搜索空间。在这项工作中,我们提议了概率结构学变迁(PSGE),这是将结构学变迁(SGE)和概率学变迁(PGE)代表变异和绘图机制结合起来的一种新方法。基因类型是一套动态列表,每个非术语表都有一个,其中每个要素都代表了选择下一个概率性环境自由语法(PCFG)衍生规则的概率。PSGE在统计上超过所研究的所有六个基准问题。与PGE相比,PSGE在所分析的6个问题中完成了4个。