{"title":"交叉验证和自举作为遗传算法目标函数的研究","authors":"E. D. Lacerda, A. Carvalho, Teresa B Ludermir","doi":"10.1109/SBRN.2002.1181451","DOIUrl":null,"url":null,"abstract":"This article addresses the problem of finding the adjustable parameters of a learning algorithm using genetic algorithms. This problem is also known as the model selection problem. Some model selection techniques (e.g., cross-validation and bootstrap) are combined with the genetic algorithms of different ways. Those combinations explore features of the genetic algorithms such as the ability for handling multiple and noise objective functions. The proposed multiobjective GA is quite general and can be applied to a large range of learning algorithms.","PeriodicalId":157186,"journal":{"name":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"A study of cross-validation and bootstrap as objective functions for genetic algorithms\",\"authors\":\"E. D. Lacerda, A. Carvalho, Teresa B Ludermir\",\"doi\":\"10.1109/SBRN.2002.1181451\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article addresses the problem of finding the adjustable parameters of a learning algorithm using genetic algorithms. This problem is also known as the model selection problem. Some model selection techniques (e.g., cross-validation and bootstrap) are combined with the genetic algorithms of different ways. Those combinations explore features of the genetic algorithms such as the ability for handling multiple and noise objective functions. The proposed multiobjective GA is quite general and can be applied to a large range of learning algorithms.\",\"PeriodicalId\":157186,\"journal\":{\"name\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBRN.2002.1181451\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBRN.2002.1181451","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A study of cross-validation and bootstrap as objective functions for genetic algorithms
This article addresses the problem of finding the adjustable parameters of a learning algorithm using genetic algorithms. This problem is also known as the model selection problem. Some model selection techniques (e.g., cross-validation and bootstrap) are combined with the genetic algorithms of different ways. Those combinations explore features of the genetic algorithms such as the ability for handling multiple and noise objective functions. The proposed multiobjective GA is quite general and can be applied to a large range of learning algorithms.