{"title":"从染色体空间到特征空间的最优映射解决序列模式识别问题","authors":"M. Zohdy, D. Bouchaffra, J. Quinlan","doi":"10.1109/MWSCAS.2001.986242","DOIUrl":null,"url":null,"abstract":"In this paper we present a method for modeling a genetic algorithm for a sequential pattern recognition problem. This genetic algorithm is shown to be useful in obtaining particular solutions; similarities between particular solutions give a general solution. Transition between chromosome space and feature space is done through relating genes to inputs, based on the discrete nature of both spaces.","PeriodicalId":403026,"journal":{"name":"Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No.01CH37257)","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Optimal mapping from chromosome space to feature space for solving sequential pattern recognition problems\",\"authors\":\"M. Zohdy, D. Bouchaffra, J. Quinlan\",\"doi\":\"10.1109/MWSCAS.2001.986242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a method for modeling a genetic algorithm for a sequential pattern recognition problem. This genetic algorithm is shown to be useful in obtaining particular solutions; similarities between particular solutions give a general solution. Transition between chromosome space and feature space is done through relating genes to inputs, based on the discrete nature of both spaces.\",\"PeriodicalId\":403026,\"journal\":{\"name\":\"Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No.01CH37257)\",\"volume\":\"6 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No.01CH37257)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS.2001.986242\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No.01CH37257)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2001.986242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal mapping from chromosome space to feature space for solving sequential pattern recognition problems
In this paper we present a method for modeling a genetic algorithm for a sequential pattern recognition problem. This genetic algorithm is shown to be useful in obtaining particular solutions; similarities between particular solutions give a general solution. Transition between chromosome space and feature space is done through relating genes to inputs, based on the discrete nature of both spaces.