{"title":"A mixed genetic approach to the optimization of neural controllers","authors":"J. Heistermann","doi":"10.1109/CMPEUR.1992.218440","DOIUrl":null,"url":null,"abstract":"The author discusses some of the capabilities of genetic algorithms (GAs). GAs are compared with other standard optimization methods like gradient descent or simulated annealing (SA). It is shown that SA is just a special case of GA. The role of a population in the optimization process is demonstrated by an example. GA was applied as a learning algorithm to neural networks.<<ETX>>","PeriodicalId":390273,"journal":{"name":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","volume":"242 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompEuro 1992 Proceedings Computer Systems and Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMPEUR.1992.218440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
The author discusses some of the capabilities of genetic algorithms (GAs). GAs are compared with other standard optimization methods like gradient descent or simulated annealing (SA). It is shown that SA is just a special case of GA. The role of a population in the optimization process is demonstrated by an example. GA was applied as a learning algorithm to neural networks.<>
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