{"title":"用遗传算法求解火星一号的映射问题","authors":"T. Kalinowski","doi":"10.1109/MPCS.1994.367057","DOIUrl":null,"url":null,"abstract":"Good mapping algorithms can significantly reduce the total execution time of a program. However, the mapping problem is NP-complete. Consequently, heuristic methods should be used. Massively parallel systems allow the implementation of genetic algorithms running on large populations. In this paper, an algorithm based on a neighbourhood model is presented. The program has been implemented on 4096-processor MasPar-1 multicomputer. Experimental results for three genetic operators are presented and compared. The influence of initialisation strategies and selection techniques is also considered. A new initialization strategy based on grouping of adjacent tasks into approximately equal clusters is proposed.<<ETX>>","PeriodicalId":64175,"journal":{"name":"专用汽车","volume":"44 1","pages":"370-374"},"PeriodicalIF":0.0000,"publicationDate":"1994-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Solving the mapping problem with a genetic algorithm on the MasPar-1\",\"authors\":\"T. Kalinowski\",\"doi\":\"10.1109/MPCS.1994.367057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Good mapping algorithms can significantly reduce the total execution time of a program. However, the mapping problem is NP-complete. Consequently, heuristic methods should be used. Massively parallel systems allow the implementation of genetic algorithms running on large populations. In this paper, an algorithm based on a neighbourhood model is presented. The program has been implemented on 4096-processor MasPar-1 multicomputer. Experimental results for three genetic operators are presented and compared. The influence of initialisation strategies and selection techniques is also considered. A new initialization strategy based on grouping of adjacent tasks into approximately equal clusters is proposed.<<ETX>>\",\"PeriodicalId\":64175,\"journal\":{\"name\":\"专用汽车\",\"volume\":\"44 1\",\"pages\":\"370-374\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"专用汽车\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.1109/MPCS.1994.367057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"专用汽车","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1109/MPCS.1994.367057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Solving the mapping problem with a genetic algorithm on the MasPar-1
Good mapping algorithms can significantly reduce the total execution time of a program. However, the mapping problem is NP-complete. Consequently, heuristic methods should be used. Massively parallel systems allow the implementation of genetic algorithms running on large populations. In this paper, an algorithm based on a neighbourhood model is presented. The program has been implemented on 4096-processor MasPar-1 multicomputer. Experimental results for three genetic operators are presented and compared. The influence of initialisation strategies and selection techniques is also considered. A new initialization strategy based on grouping of adjacent tasks into approximately equal clusters is proposed.<>
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