{"title":"遗传算法的最优种群大小","authors":"J. Alander","doi":"10.1109/CMPEUR.1992.218485","DOIUrl":null,"url":null,"abstract":"A description is given of the results of experiments to find the optimum population size for genetic algorithms as a function of problem complexity. It seems that for moderate problem complexity the optimal population size for problems coded as bitstrings is approximately the length of the string in bits for sequential machines. This result is also consistent with earlier experimentation. In parallel architectures the optimal population size is larger than in the corresponding sequential cases, but the exact figures seem to be sensitive to implementation details.<<ETX>>","PeriodicalId":390273,"journal":{"name":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","volume":"148 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"247","resultStr":"{\"title\":\"On optimal population size of genetic algorithms\",\"authors\":\"J. Alander\",\"doi\":\"10.1109/CMPEUR.1992.218485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A description is given of the results of experiments to find the optimum population size for genetic algorithms as a function of problem complexity. It seems that for moderate problem complexity the optimal population size for problems coded as bitstrings is approximately the length of the string in bits for sequential machines. This result is also consistent with earlier experimentation. In parallel architectures the optimal population size is larger than in the corresponding sequential cases, but the exact figures seem to be sensitive to implementation details.<<ETX>>\",\"PeriodicalId\":390273,\"journal\":{\"name\":\"CompEuro 1992 Proceedings Computer Systems and Software Engineering\",\"volume\":\"148 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"247\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CompEuro 1992 Proceedings Computer Systems and Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CMPEUR.1992.218485\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMPEUR.1992.218485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A description is given of the results of experiments to find the optimum population size for genetic algorithms as a function of problem complexity. It seems that for moderate problem complexity the optimal population size for problems coded as bitstrings is approximately the length of the string in bits for sequential machines. This result is also consistent with earlier experimentation. In parallel architectures the optimal population size is larger than in the corresponding sequential cases, but the exact figures seem to be sensitive to implementation details.<>
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