{"title":"利用多目标适应度函数改进笛卡尔遗传规划电路","authors":"J. Hilder, James Alfred Walker, A. Tyrrell","doi":"10.1109/AHS.2010.5546262","DOIUrl":null,"url":null,"abstract":"This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional designs.","PeriodicalId":101655,"journal":{"name":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Use of a multi-objective fitness function to improve cartesian genetic programming circuits\",\"authors\":\"J. Hilder, James Alfred Walker, A. Tyrrell\",\"doi\":\"10.1109/AHS.2010.5546262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional designs.\",\"PeriodicalId\":101655,\"journal\":{\"name\":\"2010 NASA/ESA Conference on Adaptive Hardware and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 NASA/ESA Conference on Adaptive Hardware and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AHS.2010.5546262\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AHS.2010.5546262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Use of a multi-objective fitness function to improve cartesian genetic programming circuits
This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional designs.