Desire-Driven Selection: An Epigenetic Experiment in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8464

@InProceedings{simoes:2025:GECCO,

• author = "Jose Maria Simoes and Nuno Lourenco and Penousal Machado",
• title = "Desire-Driven Selection: An Epigenetic Experiment in Genetic Programming",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "1044--1052",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, parent selection, sexual selection, mate choice",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726358",
• DOI = "doi:10.1145/3712256.3726358",
• size = "9 pages",
• abstract = "In nature, survival poses small benefits if one fails to reproduce and spread one's genes. This is particularly relevant in sexually reproductive species, which exerts another pressure dimension on the individual beyond natural selection: Sexual Selection. More often than not, the quality of the chosen mate is a crucial step in reproduction, making all the investment in mate choice worthwhile. This partly explains why partners often prefer certain secondary traits, such as ornaments, particularly if such traits signal good fitness. We hypothesize that the dynamics between mating preferences and fitness-dependent ornaments can act as a filter to find a mate within a population, exploiting good solutions while maintaining high diversity. In this work, we propose a new selection method for Genetic Programming based on these premises, validating our approach on regression problems. Results show that high levels of diversity are maintained when compared against a standard tournament selection with performance gains, reducing the overall error by 16.3\% and 13.8\% in training and testing respectively, and performing up to par with state-of-the-art Lexicase selection while also providing the best overall solution.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jose Maria da Costa Simoes Nuno Lourenco Penousal Machado

Citations