- author = "Thibaut Bellanger and Matthieu {Le Berre} and Manuel Clergue and Jin-Kao Hao",
- title = "Directed Acyclic Program Graph Applied to Supervised Classification",
- booktitle = "2nd GECCO workshop on Graph-based Genetic Programming",
- year = "2024",
- editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas",
- pages = "1676--1680",
- address = "Melbourne, Australia",
- series = "GECCO '24",
- month = "14 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, evolutionary algorithm, local search, supervised classification",
- isbn13 = "979-8-4007-0495-6",
-
DOI = "
doi:10.1145/3638530.3664115",
- size = "5 pages",
- abstract = "In the realm of Machine Learning, the pursuit of simpler yet effective models has led to increased interest in decision trees due to their interpretability and efficiency. However, their inherent simplicity often limits their ability to handle intricate patterns in data. This paper introduces a novel approach termed Directed Acyclic Graphs of Programs, inspired by evolutionary strategies, to address this challenge. By iteratively constructing program graphs from binary decision makers, our method offers a balance of simplicity and performance for classification tasks. Notably, we emphasize the preservation of model interpretability and expressiveness, avoiding the use of ensemble techniques like voting. Experimental evaluations demonstrate the superiority of our approach over existing methods in terms of both effectiveness and interpretability.",
-
notes = "wksp117s2 also known as
\cite{bellanger:2024:GECCOcomp} GECCO-2024 GGP
A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",
