{"title":"A parallel processor for distributed genetic algorithm with redundant binary number","authors":"T. Kamimura, A. Kanasugi","doi":"10.4156/IJIPM.VOL4.ISSUE1.12","DOIUrl":null,"url":null,"abstract":"Genetic algorithm (GA) is one of optimization algorithm based on an idea for evolution of life. GA can be applied various combination optimization problem. This paper proposes a parallel processor for distributed genetic algorithm (DGA) with redundant binary number. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease. Since DGA is a parallel algorithm, the performance can be improved by using a specified parallel processor. The effectiveness of the proposed processor was confirmed by some simulations and experiments using FPGA circuit board.","PeriodicalId":105832,"journal":{"name":"2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4156/IJIPM.VOL4.ISSUE1.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Genetic algorithm (GA) is one of optimization algorithm based on an idea for evolution of life. GA can be applied various combination optimization problem. This paper proposes a parallel processor for distributed genetic algorithm (DGA) with redundant binary number. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease. Since DGA is a parallel algorithm, the performance can be improved by using a specified parallel processor. The effectiveness of the proposed processor was confirmed by some simulations and experiments using FPGA circuit board.