{"title":"Solving the rectangular packing problem by an adaptive GA based on sequence-pair","authors":"Koichi Hatta, S. Wakabayashi, T. Koide","doi":"10.1109/ASPDAC.1999.759990","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a genetic algorithm (GA) to solve the rectangular packing problem (RP), in which the sequence-pair representation is adopted as the coding scheme of each chromosome. New genetic operators for RP are presented to explore the search space efficiently. The proposed GA has an adaptive strategy which dynamically selects an appropriate genetic operator during the GA execution depending on the state of an individual. Experimental results show the effectiveness of our adaptive genetic algorithm compared to simulated annealing (SA).","PeriodicalId":201352,"journal":{"name":"Proceedings of the ASP-DAC '99 Asia and South Pacific Design Automation Conference 1999 (Cat. No.99EX198)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ASP-DAC '99 Asia and South Pacific Design Automation Conference 1999 (Cat. No.99EX198)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASPDAC.1999.759990","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
In this paper, we propose a genetic algorithm (GA) to solve the rectangular packing problem (RP), in which the sequence-pair representation is adopted as the coding scheme of each chromosome. New genetic operators for RP are presented to explore the search space efficiently. The proposed GA has an adaptive strategy which dynamically selects an appropriate genetic operator during the GA execution depending on the state of an individual. Experimental results show the effectiveness of our adaptive genetic algorithm compared to simulated annealing (SA).