{"title":"用基因表达式编程进行学生创造力建模","authors":"Jinxin Qian, Jiayuan Yu","doi":"10.1109/ICCSEE.2012.390","DOIUrl":null,"url":null,"abstract":"Williams Creativity Test B (WCTB) and Adolescent Scientific Creativity Scale (ASCS) were used to measure the creative affective and scientific creativity for 550 middle school students. In these students, 70% of them were selected to be as training samples, and the others to be as testing samples. Gene expression programming (GEP), generalized regression neural network (GRNN) and multivariable linear regression (MLR) were used for modeling and testing. The result showed the fitting error of GEP model was the lowest compared with the errors of GRNN and MLR models.","PeriodicalId":132465,"journal":{"name":"2012 International Conference on Computer Science and Electronics Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Students Creativity Modeling with Gene Expression Programming\",\"authors\":\"Jinxin Qian, Jiayuan Yu\",\"doi\":\"10.1109/ICCSEE.2012.390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Williams Creativity Test B (WCTB) and Adolescent Scientific Creativity Scale (ASCS) were used to measure the creative affective and scientific creativity for 550 middle school students. In these students, 70% of them were selected to be as training samples, and the others to be as testing samples. Gene expression programming (GEP), generalized regression neural network (GRNN) and multivariable linear regression (MLR) were used for modeling and testing. The result showed the fitting error of GEP model was the lowest compared with the errors of GRNN and MLR models.\",\"PeriodicalId\":132465,\"journal\":{\"name\":\"2012 International Conference on Computer Science and Electronics Engineering\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Computer Science and Electronics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSEE.2012.390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Computer Science and Electronics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSEE.2012.390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Students Creativity Modeling with Gene Expression Programming
Williams Creativity Test B (WCTB) and Adolescent Scientific Creativity Scale (ASCS) were used to measure the creative affective and scientific creativity for 550 middle school students. In these students, 70% of them were selected to be as training samples, and the others to be as testing samples. Gene expression programming (GEP), generalized regression neural network (GRNN) and multivariable linear regression (MLR) were used for modeling and testing. The result showed the fitting error of GEP model was the lowest compared with the errors of GRNN and MLR models.
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