{"title":"A Proposal of Visualization of Multi-Objective Pareto Solutions -Development of Mining Technique for Solutions-","authors":"T. Yoshikawa, Daisuke Yamashiro, T. Furuhashi","doi":"10.1109/MCDM.2007.369433","DOIUrl":null,"url":null,"abstract":"The rapid progresses of computers introduce evolutionary computations to next step, which is the demand for the variety of Pareto solutions in multi-objective optimization problems. We can calculate a large amount of Pareto solutions in a short time. However, it is difficult to use the acquired Pareto solutions effectively, because the Pareto solutions have multi-dimension of fitness values. This study tries to develop \"mining of solutions\" technique with visualization. This paper proposes a visualizing method for Pareto solutions which have multi-objective fitness values. The proposed method enables us to grasp the distributed structure of Pareto solutions and clarify the relationship among multi-objective fitness values. This paper shows that the visualized data enables us to interpret the characteristics of Pareto solutions through experimental result","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCDM.2007.369433","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The rapid progresses of computers introduce evolutionary computations to next step, which is the demand for the variety of Pareto solutions in multi-objective optimization problems. We can calculate a large amount of Pareto solutions in a short time. However, it is difficult to use the acquired Pareto solutions effectively, because the Pareto solutions have multi-dimension of fitness values. This study tries to develop "mining of solutions" technique with visualization. This paper proposes a visualizing method for Pareto solutions which have multi-objective fitness values. The proposed method enables us to grasp the distributed structure of Pareto solutions and clarify the relationship among multi-objective fitness values. This paper shows that the visualized data enables us to interpret the characteristics of Pareto solutions through experimental result