Empirical Comparison of Runtime Improvement Approaches: Genetic Improvement, Parameter Tuning, and Their Combination

Created by W.Langdon from gp-bibliography.bib Revision:1.8208

@InProceedings{songpetchmongkol:2025:GI,

• author = "Thanatad Songpetchmongkol and Aymeric Blot and Justyna Petke",
• title = "Empirical Comparison of Runtime Improvement Approaches: Genetic Improvement, Parameter Tuning, and Their Combination",
• 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",
• URL = "https://gpbib.cs.ucl.ac.uk/gi2025/songpetchmongkol_2025_GI.pdf",
• URL = "https://solar.cs.ucl.ac.uk/pdf/songpetchmongkol_2025_GI.pdf",
• size = "8 pages",
• abstract = "Software can be optimised in various ways, e.g., by changing the code directly, modifying compiler or software paramters. From our literature review, we found that the best search strategies in genetic improvement and algorithm configuration, that generalise to both domains, are based on local search and genetic algorithms. We thus compared the two approaches for runtime improvement of the MiniSAT solver. We also explored the two search strategies on the joint search space of parameter and source code edits. We found that genetic improvement with first improvement local search led to the best results by improving MiniSAT runtime by 18.05%.",
• notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}",

}

Genetic Programming entries for Thanatad Songpetchmongkol Aymeric Blot Justyna Petke

Citations