Application of Genetic Programming to Unsolved Mathematical Problems II

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Tanemura:2023:GCCE,

• author = "Keito Tanemura and Yuji Sasaki and Shoei Takahashi and Yuki Tokuni and Hikaru Manabe and Ryohei Miyadera",
• booktitle = "2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)",
• title = "Application of Genetic Programming to Unsolved Mathematical Problems {II}",
• year = "2023",
• pages = "608--609",
• abstract = "In this research, the authors developed a new Swift programming library of symbolic regression based on genetic programming. Symbolic regression is a field of artificial intelligence where AI looks for formulas that describe the given data. The data used for the research is winning positions of combinatorial games. Compared to the library presented at the last GCCE conference, this library gets two new features to select the fittest formulae.The first is to find a minimum number of formulae that describe the given data.The second is to separate the data into smaller subsets, and find formulae to describe each subset.With these two new features, this new Swift programming library of symbolic regression can be a powerful tool in the research of mathematics and science.",
• keywords = "genetic algorithms, genetic programming, Integer programming, Electric potential, Games, Search problems, Libraries, symbolic regression, combinatorial games, mixed integer programming",
• DOI = "doi:10.1109/GCCE59613.2023.10315661",
• ISSN = "2693-0854",
• month = oct,
• notes = "Also known as \cite{10315661}",

}

Genetic Programming entries for Keito Tanemura Yuji Sasaki Shoei Takahashi Yuki Tokuni Hikaru Manabe Ryohei Miyadera

Citations