{"title":"多目标优化中质量指标的基准。","authors":"G. Lizárraga, A. H. Aguirre, S. Rionda","doi":"10.1145/1569901.1570188","DOIUrl":null,"url":null,"abstract":"Comparing the performance of different evolutive Multi-Objective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a non-dominated set is better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A benchmark for quality indicators in multi-objective optimization.\",\"authors\":\"G. Lizárraga, A. H. Aguirre, S. Rionda\",\"doi\":\"10.1145/1569901.1570188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Comparing the performance of different evolutive Multi-Objective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a non-dominated set is better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.\",\"PeriodicalId\":193093,\"journal\":{\"name\":\"Proceedings of the 11th Annual conference on Genetic and evolutionary computation\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th Annual conference on Genetic and evolutionary computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1569901.1570188\",\"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 11th Annual conference on Genetic and evolutionary computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1569901.1570188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A benchmark for quality indicators in multi-objective optimization.
Comparing the performance of different evolutive Multi-Objective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a non-dominated set is better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.