Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees

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

@InProceedings{Ito:2023:IMCOM,

• author = "Takashi Ito",
• booktitle = "2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)",
• title = "Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees",
• year = "2023",
• abstract = "In evolution, Genetic programming (GP) is proposed to obtain suitable action rules for a target problem. Because action rules are expressed in a tree structure, their meaning is easily understandable. In addition, GP with multiple tree structures has been proposed for agent learning, and a method has been proposed to decide the number of multiple trees that must be set to individuals for each target problem during evolution. In this study, we focused on an algorithm to search for the suitable number of multiple trees in evolution and introduced a method for generating individuals with conditional probability to improve performance.",
• keywords = "genetic algorithms, genetic programming, Sociology, Information management, Statistics, Evolutionary Computation, Autonomous Agent, Multiple Action Rules",
• DOI = "doi:10.1109/IMCOM56909.2023.10035600",
• month = jan,
• notes = "Also known as \cite{10035600}",

}

Genetic Programming entries for Takashi Ito

Citations