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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.

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On the Effectiveness of Crossover Operators in Cartesian Genetic Programming

  • 会议论文

  • 发表日期：

    2025年

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  • Mark Kocherovsky,
  • Marzieh Kianinejad,
  • Illya Bakurov,
  • Wolfgang Banzhaf

摘要

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.