优化重组式遗传编程，提升符号回归性能。
Improving Evaluation of Recombination-based Cartesian Genetic Programming
- 采用子图交叉与离散表型重组，替代传统变异操作。
- 在SRBench上通过超参数优化，显著提升算法表现。
- 适合关注进化算法创新的科研人员参考。
传统的笛卡尔遗传编程主要依赖变异作为其核心甚至唯一的遗传操作来驱动演化搜索。尽管近年来有所进展，但基于重组的方法长期被忽视，因其看似缺乏性能提升。本研究在符号回归基准平台SRBench上，评估了两种近期提出的重组操作：子图交叉与离散表型重组。基于TinyverseGP框架提供的实现，对这两种表示方法进行了超参数优化。结果表明，通过超参数优化，重组式笛卡尔遗传编程可获得性能提升。
原文摘要 · Abstract (English)
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.
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