{"title":"SOFTWARE EFFORT ESTIMATION USING GENETIC ALGORITHM","authors":"B. MariKumar, P. Latha, E. Praynlin","doi":"10.18000/IJISAC.50142","DOIUrl":null,"url":null,"abstract":"Mari Kumar B1, Dr. Latha P2, Praynlin E3 1,3Government College of Engineering, Tirunelveli, India 2 Department of Computer Science and Engineering, Anna University 1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com Abstract A feed forward back propagation neural network is most commonly used to the form of artificial neural network. This algorithm being a correct procedure, it accurate result in the neural network. The estimate of this method as the training of Neural Network is compared with that of genetic algorithm, that the form of based on estimate the software effort estimation. The comparison of two methods is used to accuracy of the software effort estimation.","PeriodicalId":121456,"journal":{"name":"International Journal on Information Sciences and Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Information Sciences and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18000/IJISAC.50142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mari Kumar B1, Dr. Latha P2, Praynlin E3 1,3Government College of Engineering, Tirunelveli, India 2 Department of Computer Science and Engineering, Anna University 1marikumar106@gmail.com, 2plathamuthuraj@gmail.com, 3praynlin25@gmail.com Abstract A feed forward back propagation neural network is most commonly used to the form of artificial neural network. This algorithm being a correct procedure, it accurate result in the neural network. The estimate of this method as the training of Neural Network is compared with that of genetic algorithm, that the form of based on estimate the software effort estimation. The comparison of two methods is used to accuracy of the software effort estimation.