Improving Evaluation of Recombination-based Cartesian Genetic Programming

2026-05-27

AI Summary

Conventional wisdom holds that recombination operators offer little performance benefit in Cartesian Genetic Programming (CGP), leading to their long-standing neglect. This study systematically optimizes the hyperparameters of two recombination strategies—subgraph crossover and discrete phenotypic recombination—within the TinyverseGP framework on the SRBench benchmark platform. For the first time, it demonstrates that carefully tuned recombination significantly enhances CGP’s performance on symbolic regression tasks. Challenging the prevailing paradigm that CGP relies predominantly on mutation, this work establishes that recombination, when appropriately configured, possesses substantial potential. These findings open new avenues for the design of evolutionary algorithms by reintegrating recombination as a key operator in CGP.

Abstract

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.

Problem

• Cartesian Genetic Programming
• recombination
• symbolic regression
• genetic operators
• performance evaluation

Innovation

• Cartesian Genetic Programming
• recombination
• hyperparameter optimisation
• symbolic regression
• subgraph crossover