Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long been avoided, due to apparent lack of performance gains. This study examines two recently suggested recombination-based operators, subgraph crossover and discrete phenotypic recombination on SRBench, a benchmarking platform for symbolic regression. Using the implementations provided in the TinyverseGP framework, we perform hyperparameter optimisation of the respective representations with these two operators. Our work demonstrates that hyperparameter optimisation can lead to improvements in performance for recombination-based Cartesian Genetic Programming.

翻译：笛卡尔遗传编程传统上使用变异作为其主要且往往是唯一的遗传算子来驱动进化搜索。尽管近年来有所进展，但由于缺乏性能提升，基于重组的长期被避免使用。本研究在符号回归基准平台SRBench上，考察了两种近期提出的基于重组的算子：子图交叉和离散表型重组。通过使用TinyverseGP框架中提供的实现，我们对采用这两种算子的相应表示进行了超参数优化。我们的工作表明，超参数优化能够提升基于重组的笛卡尔遗传编程的性能。