{"title":"用树数搜索方法改进遗传规划的进化性能","authors":"Takashi Ito","doi":"10.1109/IMCOM56909.2023.10035600","DOIUrl":null,"url":null,"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.","PeriodicalId":230213,"journal":{"name":"2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees\",\"authors\":\"Takashi Ito\",\"doi\":\"10.1109/IMCOM56909.2023.10035600\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":230213,\"journal\":{\"name\":\"2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMCOM56909.2023.10035600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMCOM56909.2023.10035600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
