Domain-Aware Feature Learning with Grammar-Guided Genetic Programming

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

@InProceedings{Ingelse:2023:EuroGP,

• author = "Leon Ingelse and Alcides Fonseca",
• title = "Domain-Aware Feature Learning with Grammar-Guided Genetic Programming",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "227--243",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, interpretability, Domain-aware feature learning, Historical-data aggregation, Grammar-guided genetic programming: Poster",
• isbn13 = "978-3-031-29572-0",
• URL = "https://rdcu.be/c8U0S",
• DOI = "doi:10.1007/978-3-031-29573-7_15",
• size = "17 pages",
• abstract = "Feature Learning (FL) is key to well-performing machine learning models. However, the most popular FL methods lack interpretability, which is becoming a critical requirement of Machine Learning. We propose to incorporate information from the problem domain in the structure of programs on top of the existing M3GP approach. This technique, named Domain-Knowledge M3GP, works by defining the possible feature transformations using a grammar through Grammar-Guided Genetic Programming. While requiring the user to specify the domain knowledge, this approach has the advantage of limiting the search space, excluding programs that make no sense to humans. We extend this approach with the possibility of introducing complex, aggregating queries over historic data. This extension allows to expand the search space to include relevant programs that were not possible before. We evaluate our methods on performance and interpretability in 6 use cases, showing promising results in both areas. We conclude that performance and interpretability of FL methods can benefit from domain-knowledge incorporation and aggregation, and give guidelines on when to use them.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Leon Ingelse Alcides Fonseca

Citations