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This paper presents an approach for the automatic synthesis of a rank-based selection operator in a binary genetic algorithm using the FunSearchmethod, which combines the capabilities of large language models and evolutionary search. Instead of manually designing the rank selection scheme, the algorithm autonomously discovers an implementation of the ranking function. The developed algorithm is tested on pseudo-Boolean optimization problems using 17 functions from the IOHprofiler benchmark suite. The results show that the automatically discovered operators achieve higher average fitness and stability compared to the classical scheme.

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Automatic Synthesis of Selection Operators in Genetic Algorithms Using FunSearch

  • 会议论文

  • 发表日期：

    2026年

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  • Aleksandra Korableva,
  • Vladimir Stanovov

摘要

This paper presents an approach for the automatic synthesis of a rank-based selection operator in a binary genetic algorithm using the FunSearchmethod, which combines the capabilities of large language models and evolutionary search. Instead of manually designing the rank selection scheme, the algorithm autonomously discovers an implementation of the ranking function. The developed algorithm is tested on pseudo-Boolean optimization problems using 17 functions from the IOHprofiler benchmark suite. The results show that the automatically discovered operators achieve higher average fitness and stability compared to the classical scheme.