{"title":"On programming an adaptable network of molecular processing elements","authors":"K. Akingbehin","doi":"10.1109/IEMBS.1988.95323","DOIUrl":null,"url":null,"abstract":"To utilize the computational style and massive parallelism provided by molecular computing devices, conventional techniques which individually program molecules need to be augmented by techniques which collectively program a network of molecules. The author describes such an evolutionary programming technique. It consists of a training phase, in which the network is trained to recognize known patterns, and a performing phase, in which the network is presented with unknown patterns. A description is given of the results obtained when the technique is applied to a simulated network of computing devices. The computational tasks performed are those which have been traditionally difficult for conventional programming techniques.<<ETX>>","PeriodicalId":227170,"journal":{"name":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1988.95323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
To utilize the computational style and massive parallelism provided by molecular computing devices, conventional techniques which individually program molecules need to be augmented by techniques which collectively program a network of molecules. The author describes such an evolutionary programming technique. It consists of a training phase, in which the network is trained to recognize known patterns, and a performing phase, in which the network is presented with unknown patterns. A description is given of the results obtained when the technique is applied to a simulated network of computing devices. The computational tasks performed are those which have been traditionally difficult for conventional programming techniques.<>