{"title":"一种基于勘探开发平衡的最优特征因子估计方法","authors":"Kun Li, Ming Li, Hao Chen","doi":"10.1109/ICINFA.2013.6720318","DOIUrl":null,"url":null,"abstract":"Under the background of the balance of exploration and exploitation, this paper analyzes the causes of the optimization hardness by using the concepts of effective ratio of exploration and exploitation. Then, a novel method is proposed to estimate the optimal feature factor, which is the essential part of optimal contraction theorem. At last, the method is tested with eight test functions. Some disadvantages of optimal feature factors are found by analysis the test results.","PeriodicalId":250844,"journal":{"name":"2013 IEEE International Conference on Information and Automation (ICIA)","volume":"12 10","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An estimation method of optimal feature factor based on the balance of exploration and exploitation\",\"authors\":\"Kun Li, Ming Li, Hao Chen\",\"doi\":\"10.1109/ICINFA.2013.6720318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Under the background of the balance of exploration and exploitation, this paper analyzes the causes of the optimization hardness by using the concepts of effective ratio of exploration and exploitation. Then, a novel method is proposed to estimate the optimal feature factor, which is the essential part of optimal contraction theorem. At last, the method is tested with eight test functions. Some disadvantages of optimal feature factors are found by analysis the test results.\",\"PeriodicalId\":250844,\"journal\":{\"name\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"volume\":\"12 10\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2013.6720318\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2013.6720318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An estimation method of optimal feature factor based on the balance of exploration and exploitation
Under the background of the balance of exploration and exploitation, this paper analyzes the causes of the optimization hardness by using the concepts of effective ratio of exploration and exploitation. Then, a novel method is proposed to estimate the optimal feature factor, which is the essential part of optimal contraction theorem. At last, the method is tested with eight test functions. Some disadvantages of optimal feature factors are found by analysis the test results.