On the Effectiveness of Crossover Operators in Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8229

@InProceedings{kocherovsky:2025:EuroGP,

• author = "Mark Kocherovsky and Kianinejad Marzieh and Illya Bakurov and Wolfgang Banzhaf",
• title = "On the Effectiveness of Crossover Operators in Cartesian Genetic Programming",
• booktitle = "European Conference on Genetic Programming, EuroGP 2025",
• year = "2025",
• editor = "Bing Xue and Luca Manzoni and Illya Bakurov",
• series = "LNCS",
• address = "Trieste",
• month = "23-25 " # apr,
• publisher = "Springer Nature",
• note = "Forthcoming",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Crossover, Neural Crossover, Crossover Effects, Reinforcement Learning",
• abstract = "This study investigates the effectiveness of Cartesian Genetic Programming by analysing 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 uses 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.",
• notes = "also known as \cite{paper_109_cartesian_genetic_programming}

  Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025",

}

Genetic Programming entries for Mark Kocherovsky Kianinejad Marzieh Illya Bakurov Wolfgang Banzhaf

Citations