{"title":"论进化电路设计中的元件协同进化","authors":"Michaela Sikulová, Gergely Komjathy, L. Sekanina","doi":"10.1109/ICES.2014.7008735","DOIUrl":null,"url":null,"abstract":"A divide and conquer approach is one of the methods introduced to get over the scalability problem of the evolutionary circuit design. A complex circuit is decomposed into modules which are evolved separately and without any interaction. The benefits are in reducing the search space and accelerating the evaluation of candidate circuits. In this paper, the evolution of non-interacting modules is replaced by a coevolutionary algorithm, in which the fitness of a module depends on fitness values of other modules, i.e. the modules are adapted to work together. The proposed method is embedded into Cartesian genetic programming (CGP). The coevolutionary approach was evaluated in the design of a switching image filter which was decomposed into the filtering module and detector module. The filters evolved using the proposed coevolutionary method show a higher quality of filtering in comparison with filters utilizing independently evolved modules. Furthermore, the whole design process was accelerated 1.31 times in comparison with the standard CGP.","PeriodicalId":432958,"journal":{"name":"2014 IEEE International Conference on Evolvable Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Towards compositional coevolution in evolutionary circuit design\",\"authors\":\"Michaela Sikulová, Gergely Komjathy, L. Sekanina\",\"doi\":\"10.1109/ICES.2014.7008735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A divide and conquer approach is one of the methods introduced to get over the scalability problem of the evolutionary circuit design. A complex circuit is decomposed into modules which are evolved separately and without any interaction. The benefits are in reducing the search space and accelerating the evaluation of candidate circuits. In this paper, the evolution of non-interacting modules is replaced by a coevolutionary algorithm, in which the fitness of a module depends on fitness values of other modules, i.e. the modules are adapted to work together. The proposed method is embedded into Cartesian genetic programming (CGP). The coevolutionary approach was evaluated in the design of a switching image filter which was decomposed into the filtering module and detector module. The filters evolved using the proposed coevolutionary method show a higher quality of filtering in comparison with filters utilizing independently evolved modules. Furthermore, the whole design process was accelerated 1.31 times in comparison with the standard CGP.\",\"PeriodicalId\":432958,\"journal\":{\"name\":\"2014 IEEE International Conference on Evolvable Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Evolvable Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICES.2014.7008735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Evolvable Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICES.2014.7008735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards compositional coevolution in evolutionary circuit design
A divide and conquer approach is one of the methods introduced to get over the scalability problem of the evolutionary circuit design. A complex circuit is decomposed into modules which are evolved separately and without any interaction. The benefits are in reducing the search space and accelerating the evaluation of candidate circuits. In this paper, the evolution of non-interacting modules is replaced by a coevolutionary algorithm, in which the fitness of a module depends on fitness values of other modules, i.e. the modules are adapted to work together. The proposed method is embedded into Cartesian genetic programming (CGP). The coevolutionary approach was evaluated in the design of a switching image filter which was decomposed into the filtering module and detector module. The filters evolved using the proposed coevolutionary method show a higher quality of filtering in comparison with filters utilizing independently evolved modules. Furthermore, the whole design process was accelerated 1.31 times in comparison with the standard CGP.