2605.28353May 27, 2026Accepted for presentation as workshop paper in the graph-based genetic programming workshop (GGP) at the Genetic and Evolutionary Computation Conference (GECCO). To appear in the GECCO'26 conference companion. GECCO'26 will be held July 13-17, 2026 in San Jose, Costa Rica

Improving Evaluation of Recombination-based Cartesian Genetic Programming

Duy Long Tran, Anja Jankovic, Marie Anastacio, Holger Hoos, Roman Kalkreuth

Neural and Evolutionary Computing Artificial Intelligence Symbolic Computation

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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.

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