Contribution of Probabilistic Structured Grammatical Evolution to efficient exploration of the search space. A case study in glucose prediction

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

@InProceedings{megane:2025:GECCO,

• author = "Jessica Megane and Nuno Lourenco and J. Ignacio Hidalgo and Penousal Machado",
• title = "Contribution of Probabilistic Structured Grammatical Evolution to efficient exploration of the search space. A case study in glucose prediction",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Roman Kalkreuth and Alexander Brownlee",
• pages = "1433--1442",
• 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, grammatical evolution, Real World Applications",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726444",
• DOI = "doi:10.1145/3712256.3726444",
• size = "10 pages",
• abstract = "People with Type 1 diabetes need to predict their blood glucose levels regularly to keep them within a safe range. Accurate predictions help prevent short-term issues like hypoglycemia and reduce the risk of long-term complications. Evolutionary algorithms have shown potential for this task by generating reliable models for glucose prediction.This work compares four evolutionary approaches: Structured Grammatical Evolution (SGE), a float-based variant (SGEF), and two probabilistic methods, Probabilistic SGE (PSGE) and Co-evolutionary PSGE (Co-PSGE). These methods are tested on their ability to predict glucose levels two hours ahead in individuals with diabetes. Two aspects are examined: predictive performance and the diversity of the phenotypes produced by each approach.Results indicate that SGEF provides statistically better performance than the other methods. Although PSGE and Co-PSGE do not show statistically significant improvements in prediction accuracy, they generate a broader set of solutions and explore more distinct areas of the search space.",
• notes = "GECCO-2025 RWA A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jessica Megane Nuno Lourenco Jose Ignacio Hidalgo Perez Penousal Machado

Citations