{"title":"一种简单的多目标优化进化算法","authors":"C. L. Valenzuela","doi":"10.1109/CEC.2002.1007014","DOIUrl":null,"url":null,"abstract":"A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"118","resultStr":"{\"title\":\"A simple evolutionary algorithm for multi-objective optimization (SEAMO)\",\"authors\":\"C. L. Valenzuela\",\"doi\":\"10.1109/CEC.2002.1007014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.\",\"PeriodicalId\":184547,\"journal\":{\"name\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"volume\":\"117 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"118\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2002.1007014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2002.1007014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simple evolutionary algorithm for multi-objective optimization (SEAMO)
A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.