{"title":"一种新的基于遗传算法的约束比赛选择多准则优化方法","authors":"O. Andrzej, K. Stanislaw","doi":"10.1109/CEC.2000.870338","DOIUrl":null,"url":null,"abstract":"A new genetic algorithm based method for solving nonlinear multicriterion optimization problems is described. The method does not use a fitness value as a measure, as a genetic algorithm uses to create the population of chromosomes for the next generation. The proposed method uses tournament selection which does not require evaluation of fitness values in order to create a new population of chromosomes for the next generation. The tournament is arranged such that objective functions are evaluated only for feasible solutions. After a detailed description of the method two examples are presented and the results are compared with those obtained using other methods. This comparison shows the effectiveness of the proposed method.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"A new constraint tournament selection method for multicriteria optimization using genetic algorithm\",\"authors\":\"O. Andrzej, K. Stanislaw\",\"doi\":\"10.1109/CEC.2000.870338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new genetic algorithm based method for solving nonlinear multicriterion optimization problems is described. The method does not use a fitness value as a measure, as a genetic algorithm uses to create the population of chromosomes for the next generation. The proposed method uses tournament selection which does not require evaluation of fitness values in order to create a new population of chromosomes for the next generation. The tournament is arranged such that objective functions are evaluated only for feasible solutions. After a detailed description of the method two examples are presented and the results are compared with those obtained using other methods. This comparison shows the effectiveness of the proposed method.\",\"PeriodicalId\":218136,\"journal\":{\"name\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2000.870338\",\"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 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2000.870338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new constraint tournament selection method for multicriteria optimization using genetic algorithm
A new genetic algorithm based method for solving nonlinear multicriterion optimization problems is described. The method does not use a fitness value as a measure, as a genetic algorithm uses to create the population of chromosomes for the next generation. The proposed method uses tournament selection which does not require evaluation of fitness values in order to create a new population of chromosomes for the next generation. The tournament is arranged such that objective functions are evaluated only for feasible solutions. After a detailed description of the method two examples are presented and the results are compared with those obtained using other methods. This comparison shows the effectiveness of the proposed method.