On the Effectiveness of Crossover Operators in Cartesian Genetic Programming

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Mark Kocherovsky, Marzieh Kianinejad, Illya Bakurov, Wolfgang Banzhaf

Published: 2025, Last Modified: 28 Feb 2026EuroGP 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This study investigates the effectiveness of Cartesian Genetic Programming by analyzing numerous indicators of evolutionary dynamics when using different crossover operators and the canonical mutation-only (\(1+4\)) strategy. Specifically, we examine a traditional crossover operator which is based on the random selection of parental genes; Subgraph Crossover, where points in the range of active nodes are considered; and the recently-proposed Deep Neural Crossover (DNC) approach which utilizes a transformer network to learn correlations between genes and predict potentially beneficial crossover points. The performance of these different crossovers is evaluated on 11 standard and one real-world regression problem.