{"title":"锦标赛粒子群优化","authors":"W. H. Duminy, A. Engelbrecht","doi":"10.1109/CIG.2007.368091","DOIUrl":null,"url":null,"abstract":"This paper introduces tournament particle swarm optimization (PSO) as a method to optimize weights of game tree evaluation functions in a competitive environment using particle swarm optimization. This method makes use of tournaments to ensure a fair evaluation of the performance of particles in the swarm, relative to that of other particles. The empirical work presented compares the performance of different tournament methods that can be applied to the tournament PSO, with application to Checkers.","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Tournament Particle Swarm Optimization\",\"authors\":\"W. H. Duminy, A. Engelbrecht\",\"doi\":\"10.1109/CIG.2007.368091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces tournament particle swarm optimization (PSO) as a method to optimize weights of game tree evaluation functions in a competitive environment using particle swarm optimization. This method makes use of tournaments to ensure a fair evaluation of the performance of particles in the swarm, relative to that of other particles. The empirical work presented compares the performance of different tournament methods that can be applied to the tournament PSO, with application to Checkers.\",\"PeriodicalId\":365269,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2007.368091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence and Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2007.368091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper introduces tournament particle swarm optimization (PSO) as a method to optimize weights of game tree evaluation functions in a competitive environment using particle swarm optimization. This method makes use of tournaments to ensure a fair evaluation of the performance of particles in the swarm, relative to that of other particles. The empirical work presented compares the performance of different tournament methods that can be applied to the tournament PSO, with application to Checkers.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
