{"title":"基于质量指标集成的象棋分级系统的多目标进化算法排序","authors":"Miha Ravber, M. Mernik, M. Črepinšek","doi":"10.1109/CEC.2017.7969481","DOIUrl":null,"url":null,"abstract":"Evolutionary Algorithms have been applied successfully for solving real-world multi-objective problems which explains the influx of newly proposed Multi-Objective Evolutionary Algorithms (MOEAs). In order to determine their performance, comparison with existing algorithms must be conducted. However, conducting a comparison is not a trivial task. Benchmark functions must be selected and the results have to be analyzed using a statistical method. In addition, the results of MOEAs can be evaluated with different Quality Indicators (QIs), which aggravates the comparison additionally. In this paper, we present a chess rating system which was adapted for ranking MOEAs with a Quality Indicator ensemble. The ensemble ensures that different aspects of quality are evaluated of the resulting approximation sets. The chess rating system is compared with an existing method which uses a double-elimination tournament and a quality indicator ensemble. Experimental results show that the chess rating system achieved similar rankings with fewer runs of MOEAs.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Ranking Multi-Objective Evolutionary Algorithms using a chess rating system with Quality Indicator ensemble\",\"authors\":\"Miha Ravber, M. Mernik, M. Črepinšek\",\"doi\":\"10.1109/CEC.2017.7969481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evolutionary Algorithms have been applied successfully for solving real-world multi-objective problems which explains the influx of newly proposed Multi-Objective Evolutionary Algorithms (MOEAs). In order to determine their performance, comparison with existing algorithms must be conducted. However, conducting a comparison is not a trivial task. Benchmark functions must be selected and the results have to be analyzed using a statistical method. In addition, the results of MOEAs can be evaluated with different Quality Indicators (QIs), which aggravates the comparison additionally. In this paper, we present a chess rating system which was adapted for ranking MOEAs with a Quality Indicator ensemble. The ensemble ensures that different aspects of quality are evaluated of the resulting approximation sets. The chess rating system is compared with an existing method which uses a double-elimination tournament and a quality indicator ensemble. Experimental results show that the chess rating system achieved similar rankings with fewer runs of MOEAs.\",\"PeriodicalId\":335123,\"journal\":{\"name\":\"2017 IEEE Congress on Evolutionary Computation (CEC)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Congress on Evolutionary Computation (CEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2017.7969481\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2017.7969481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ranking Multi-Objective Evolutionary Algorithms using a chess rating system with Quality Indicator ensemble
Evolutionary Algorithms have been applied successfully for solving real-world multi-objective problems which explains the influx of newly proposed Multi-Objective Evolutionary Algorithms (MOEAs). In order to determine their performance, comparison with existing algorithms must be conducted. However, conducting a comparison is not a trivial task. Benchmark functions must be selected and the results have to be analyzed using a statistical method. In addition, the results of MOEAs can be evaluated with different Quality Indicators (QIs), which aggravates the comparison additionally. In this paper, we present a chess rating system which was adapted for ranking MOEAs with a Quality Indicator ensemble. The ensemble ensures that different aspects of quality are evaluated of the resulting approximation sets. The chess rating system is compared with an existing method which uses a double-elimination tournament and a quality indicator ensemble. Experimental results show that the chess rating system achieved similar rankings with fewer runs of MOEAs.