Comparing Individual Representations in Grammar-Guided Genetic Programming for Glucose Prediction in People with Diabetes

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{ingelse:2023:GEWS2023,

• author = "Leon Ingelse and Jose-Ignacio Hidalgo and Jose Manuel Colmenar and Nuno Lourenco and Alcides Fonseca",
• title = "Comparing Individual Representations in {Grammar-Guided} Genetic Programming for Glucose Prediction in People with Diabetes",
• booktitle = "Grammatical Evolution Workshop - 25 years of GE",
• year = "2023",
• editor = "Conor Ryan and Mahsa Mahdinejad and Aidan Murphy",
• pages = "2013--2021",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, individual representations, grammar-guided genetic programming, symbolic regression",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596315",
• size = "9 pages",
• abstract = "The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In this work, we investigate the evolutionary process of three Grammar-Guided GP (GGGP) methods, Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), in the context of the complex, real-world problem of predicting the glucose level of people with diabetes two hours ahead of time. Our analysis differs from previous analyses by (1) comparing all three methods on a complex benchmark, (2) implementing the methods in the same framework, allowing a fairer comparison, and (3) analyzing the evolutionary process outside of performance. We conclude that representation choice is more impactful with a higher maximum depth, and that CFG-GP better explores the search space for deeper trees, achieving better results. Furthermore, we find that CFG-GP relies more on feature construction, whereas GE and SGE rely more on feature selection. Finally, we altered the GGGP methods in two ways: using ε-lexicase selection, which solved the overfitting problem of CFG-GP; and with a penalization of complex trees, to create more interpretable trees. Combining ε-lexicase selection with CFG-GP performed best.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Leon Ingelse Jose-Ignacio Hidalgo J Manuel Colmenar Nuno Lourenco Alcides Fonseca

Citations