{"title":"提高后代适合度的交叉操作","authors":"C. Mohan","doi":"10.1109/CEC.1999.782667","DOIUrl":null,"url":null,"abstract":"Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Crossover operators that improve offspring fitness\",\"authors\":\"C. Mohan\",\"doi\":\"10.1109/CEC.1999.782667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.\",\"PeriodicalId\":292523,\"journal\":{\"name\":\"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.1999.782667\",\"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 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.1999.782667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crossover operators that improve offspring fitness
Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.