LLM-Assisted Crossover in Genetic Improvement of Software

Created by W.Langdon from gp-bibliography.bib Revision:1.8237

@InProceedings{bouras:2025:GI,

• author = "Dimitrios Stamatios Bouras and Justyna Petke and Sergey Mechtaev",
• title = "LLM-Assisted Crossover in Genetic Improvement of Software",
• booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025",
• year = "2025",
• editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss",
• address = "Ottawa",
• month = "27 " # apr,
• note = "forthcoming",
• keywords = "genetic algorithms, genetic programming, Genetic Improvement, MAGPIE, Large Language Models, LLM, ANN",
• URL = "https://gpbib.cs.ucl.ac.uk/gi2025/bouras_2025_GI.pdf",
• URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers",
• size = "8 pages",
• abstract = "We evaluated against five traditional crossover methods across seven benchmarks, measuring performance on four key metrics: average ranking, best variant execution time, efficiency in reaching performance milestones, and viable variant count. Results show that LLM-assisted crossover achieved an average ranking of 2.27 (on a scale where 1 is best and 6 is worst), making it the top-performing method across benchmarks based on the quality of the optimal variants produced. The LLM-based approach also improved the fitness (execution time) by an average of 8.5% over the best variant produced by the traditional methods. In terms of efficiency, the LLM-assisted crossover required on average 25.6% fewer variants to reach 25%, 50%, 75%, and 100% of the final performance improvement, compared to the traditional methods. Additionally, the LLM-assisted crossover produced 4.8% more viable variants across scenarios, including both source code modification and parameter tuning cases. These findings suggest that LLMs can significantly enhance genetic programming by guiding the crossover process toward more effective and viable solutions, providing motivation for further research in LLM-assisted evolutionary algorithms.",
• notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}",

}

Genetic Programming entries for Dimitrios Stamatios Bouras Justyna Petke Sergey Mechtaev

Citations