{"title":"Artificial neurogenesis: an application to autonomous robotics","authors":"O. Michel, P. Collard","doi":"10.1109/TAI.1996.560453","DOIUrl":null,"url":null,"abstract":"A lot of research papers focus on the challenging problem of the combination of genetic algorithms and artificial neural networks. Developmental and molecular biology may be a source of inspiration for designing powerful artificial neurogenesis systems allowing the generation of complex modular structures. This paper describes in detail such a neurogenesis model associated with an evolutionary process and its application to the control of a mobile robot. Early results demonstrate the surprising efficiency of this methodology and give hints to continue the research towards the generation of more complex adaptive neural networks.","PeriodicalId":209171,"journal":{"name":"Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1996.560453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
A lot of research papers focus on the challenging problem of the combination of genetic algorithms and artificial neural networks. Developmental and molecular biology may be a source of inspiration for designing powerful artificial neurogenesis systems allowing the generation of complex modular structures. This paper describes in detail such a neurogenesis model associated with an evolutionary process and its application to the control of a mobile robot. Early results demonstrate the surprising efficiency of this methodology and give hints to continue the research towards the generation of more complex adaptive neural networks.