{"title":"遗传算法的进一步改进","authors":"Ian Stewart, Wenying Feng, S. Akl","doi":"10.1109/ITNG.2009.240","DOIUrl":null,"url":null,"abstract":"In this paper, a new genetic algorithm is developed based on a pre-existing implementation. The new algorithm requires less human interaction through the use of dynamically selected weight and acceptance probability parameters. The algorithm is implemented and tested using six benchmark functions. Results show that the new algorithm significantly outperforms other genetic algorithms in less time and with less human interaction.","PeriodicalId":347761,"journal":{"name":"2009 Sixth International Conference on Information Technology: New Generations","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Further Improvement on a Genetic Algorithm\",\"authors\":\"Ian Stewart, Wenying Feng, S. Akl\",\"doi\":\"10.1109/ITNG.2009.240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new genetic algorithm is developed based on a pre-existing implementation. The new algorithm requires less human interaction through the use of dynamically selected weight and acceptance probability parameters. The algorithm is implemented and tested using six benchmark functions. Results show that the new algorithm significantly outperforms other genetic algorithms in less time and with less human interaction.\",\"PeriodicalId\":347761,\"journal\":{\"name\":\"2009 Sixth International Conference on Information Technology: New Generations\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Sixth International Conference on Information Technology: New Generations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNG.2009.240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Sixth International Conference on Information Technology: New Generations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNG.2009.240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, a new genetic algorithm is developed based on a pre-existing implementation. The new algorithm requires less human interaction through the use of dynamically selected weight and acceptance probability parameters. The algorithm is implemented and tested using six benchmark functions. Results show that the new algorithm significantly outperforms other genetic algorithms in less time and with less human interaction.
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