{"title":"An Adaptive Genetic Based Cognitive Radio Parameter Adjustment Algorithm","authors":"A. Sun, Tao Liang, Yajun Zhang, Wei Lu","doi":"10.1109/ISCID.2014.203","DOIUrl":null,"url":null,"abstract":"To overcome the drawbacks such as pre-maturity and the inclination to converge to partial optimum of the standard genetic algorithm, the adaptive genetic algorithm has been proposed in this paper. The adaptive genetic algorithm can change its cross-over probability and mutation probability adaptively according to the iterative times and the value of the cost function to avoid the shortcomings of the standard genetic algorithm. The paper also analyses the dynamic reconfiguration problem in the cognitive radio system which is a key aspect in realizing the optimization of wireless resources management. At last, the proposed algorithm is simulated under three different modes of the OFDM multi-carrier system. The simulation results indicate that the adaptive genetic algorithm can overcome the drawbacks of the standard genetic algorithm effectively, the parameters adjustment outcomes coincide with the expected results.","PeriodicalId":385391,"journal":{"name":"2014 Seventh International Symposium on Computational Intelligence and Design","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Seventh International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2014.203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
To overcome the drawbacks such as pre-maturity and the inclination to converge to partial optimum of the standard genetic algorithm, the adaptive genetic algorithm has been proposed in this paper. The adaptive genetic algorithm can change its cross-over probability and mutation probability adaptively according to the iterative times and the value of the cost function to avoid the shortcomings of the standard genetic algorithm. The paper also analyses the dynamic reconfiguration problem in the cognitive radio system which is a key aspect in realizing the optimization of wireless resources management. At last, the proposed algorithm is simulated under three different modes of the OFDM multi-carrier system. The simulation results indicate that the adaptive genetic algorithm can overcome the drawbacks of the standard genetic algorithm effectively, the parameters adjustment outcomes coincide with the expected results.