{"title":"An Improved Immune Genetic Algorithm Based on Niche Algorithm and Its Application","authors":"Lili Dong, C. Xue, Guohua Li","doi":"10.1109/IEEC.2010.5533280","DOIUrl":null,"url":null,"abstract":"In order to overcome traditional genetic algorithm (GA)'s deficiency of slow convergence, and Niche algorithm's too fast convergence, this paper presents a new Improved Immune Genetic Algorithm (IIGA) based on the improved Niche algorithm. Firstly, the improved Niche algorithm, including convergence function, and \"noise\" chromosome, is given. Then based on the proposed flowchart of IIGA, the steps of the algorithm are introduced in detail. Finally, the IIGA is exemplified, and proved to be feasible and effective by comparing with self-adaptive Genetic Algorithm(SAGA) and traditional GA.","PeriodicalId":307678,"journal":{"name":"2010 2nd International Symposium on Information Engineering and Electronic Commerce","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Symposium on Information Engineering and Electronic Commerce","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEC.2010.5533280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In order to overcome traditional genetic algorithm (GA)'s deficiency of slow convergence, and Niche algorithm's too fast convergence, this paper presents a new Improved Immune Genetic Algorithm (IIGA) based on the improved Niche algorithm. Firstly, the improved Niche algorithm, including convergence function, and "noise" chromosome, is given. Then based on the proposed flowchart of IIGA, the steps of the algorithm are introduced in detail. Finally, the IIGA is exemplified, and proved to be feasible and effective by comparing with self-adaptive Genetic Algorithm(SAGA) and traditional GA.