A Better Multi-Objective GP-GOMEA - But do we Need it?

Created by W.Langdon from gp-bibliography.bib Revision:1.8519

@InProceedings{harrison:2025:GECCOcomp,

• author = "Joe Harrison and Tanja Alderliesten and Peter A. N. Bosman",
• title = "A Better Multi-Objective {GP-GOMEA} - But do we Need it?",
• booktitle = "Evolutionary Computation and Explainable AI",
• year = "2025",
• editor = "Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and John McCall and David Walker",
• pages = "1992--2000",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, GOMEA, symbolic regression, multi-objective optimization, explainable AI, automatically defined functions, XAI",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734302",
• DOI = "doi:10.1145/3712255.3734302",
• size = "9 pages",
• abstract = "In Symbolic Regression (SR), achieving a proper balance between accuracy and interpretability remains a key challenge. The Genetic Programming variant of the Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is of particular interest as it achieves state-of-the-art performance using a template that limits the size of expressions. A recently introduced expansion, modular GP-GOMEA, is capable of decomposing expressions using multiple subexpressions, further increasing chances of interpretability. However, modular GP-GOMEA may create larger expressions, increasing the need to balance size and accuracy. A multi-objective variant of GP-GOMEA exists, which can be used, for instance, to optimize for size and accuracy simultaneously, discovering their trade-off. However, even with enhancements that we propose in this paper to improve the performance of multi-objective modular GP-GOMEA, when optimizing for size and accuracy, the single-objective version in which a multi-objective archive is used only for logging, still consistently finds a better average hypervolume. We consequently analyze when a single-objective approach should be preferred. Additionally, we explore an objective that stimulates re-use in multi-objective modular GP-GOMEA.",
• notes = "GECCO-2025 ECXAI workshop A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Joe Harrison Tanja Alderliesten Peter A N Bosman

Citations