Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, D. Tauritz
{"title":"Preference-based multi-objective software modelling","authors":"Mohamed Wiem Mkaouer, M. Kessentini, Slim Bechikh, D. Tauritz","doi":"10.1109/CMSBSE.2013.6605712","DOIUrl":null,"url":null,"abstract":"In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer) preferences (e.g., quality, correctness, etc.) in multi-objective optimization techniques by restricting the Pareto front to a region of interest easing the decision making task. We discuss the different challenges and potential benefits of P-EMO in software modelling. We report experiments on the use of P-EMO on a well-known modeling problem where very promising results are obtained.","PeriodicalId":193450,"journal":{"name":"2013 1st International Workshop on Combining Modelling and Search-Based Software Engineering (CMSBSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 1st International Workshop on Combining Modelling and Search-Based Software Engineering (CMSBSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMSBSE.2013.6605712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer) preferences (e.g., quality, correctness, etc.) in multi-objective optimization techniques by restricting the Pareto front to a region of interest easing the decision making task. We discuss the different challenges and potential benefits of P-EMO in software modelling. We report experiments on the use of P-EMO on a well-known modeling problem where very promising results are obtained.