{"title":"使用附加参数对遗传算法进行修改,使其计算效率更高","authors":"B. Sridharan","doi":"10.1109/IADCC.2010.5423037","DOIUrl":null,"url":null,"abstract":"This paper describes a novel approach towards the modification of Genetic Algorithms. The novelty of the modified Genetic Algorithm lies in the addition of a new parameter called the age of the chromosome that would select its ability to reproduce. Also, the concept of dynamic population and elitism size has been introduced. The modified Genetic Algorithm converges to the near optimum value at a faster rate, i.e. lesser number of generations are required for the convergence and due to the concept of dynamic population size the results obtained are more accurate. Thus, the modified algorithm is observed to be computationally more efficient. The algorithm was tested for some standard functions and curves and the results were found to be highly satisfactory.","PeriodicalId":249763,"journal":{"name":"2010 IEEE 2nd International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Modifications in Genetic Algorithm using additional parameters to make them computationally efficient\",\"authors\":\"B. Sridharan\",\"doi\":\"10.1109/IADCC.2010.5423037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a novel approach towards the modification of Genetic Algorithms. The novelty of the modified Genetic Algorithm lies in the addition of a new parameter called the age of the chromosome that would select its ability to reproduce. Also, the concept of dynamic population and elitism size has been introduced. The modified Genetic Algorithm converges to the near optimum value at a faster rate, i.e. lesser number of generations are required for the convergence and due to the concept of dynamic population size the results obtained are more accurate. Thus, the modified algorithm is observed to be computationally more efficient. The algorithm was tested for some standard functions and curves and the results were found to be highly satisfactory.\",\"PeriodicalId\":249763,\"journal\":{\"name\":\"2010 IEEE 2nd International Advance Computing Conference (IACC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 2nd International Advance Computing Conference (IACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IADCC.2010.5423037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 2nd International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IADCC.2010.5423037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modifications in Genetic Algorithm using additional parameters to make them computationally efficient
This paper describes a novel approach towards the modification of Genetic Algorithms. The novelty of the modified Genetic Algorithm lies in the addition of a new parameter called the age of the chromosome that would select its ability to reproduce. Also, the concept of dynamic population and elitism size has been introduced. The modified Genetic Algorithm converges to the near optimum value at a faster rate, i.e. lesser number of generations are required for the convergence and due to the concept of dynamic population size the results obtained are more accurate. Thus, the modified algorithm is observed to be computationally more efficient. The algorithm was tested for some standard functions and curves and the results were found to be highly satisfactory.