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

Jessica Mégane, Nuno Lourenço, J. Ignacio Hidalgo, and Penousal Machado

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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.

DOI

https://doi.org/10.1145/3712256.3726444

Recommended citation

Jessica Mégane, Nuno Lourenço, J. Ignacio Hidalgo, and Penousal Machado. 2025. Contribution of Probabilistic Structured Grammatical Evolution to efficient exploration of the search space. A case study in glucose prediction. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘25). Association for Computing Machinery, New York, NY, USA, 1433–1442. https://doi.org/10.1145/3712256.3726444