{"title":"基于遗传规划的分布式嵌入式系统软硬件协同迭代改进算法","authors":"Adam Górski, M. Ogorzałek","doi":"10.5220/0010391501200125","DOIUrl":null,"url":null,"abstract":"In this work we present a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. The approach starts from a ready solution which is an embryo of a genotype. Other nodes in the genotypes are chromosomes. The chromosomes contain system refinement options. The final solution is obtained after evolution process and mapping genotype to phenotype. Unlike existing genetic programming iterative improvement methodologies our algorithm starts from randomly generated system. Therefore the search space is not constrained by any initial condition. It is also easier for the algorithm to escape local minima of optimizing parameters.","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"1 1","pages":"120-125"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Genetic Programming based Iterative Improvement Algorithm for HW/SW Cosynthesis of Distributted Embedded Systems\",\"authors\":\"Adam Górski, M. Ogorzałek\",\"doi\":\"10.5220/0010391501200125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we present a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. The approach starts from a ready solution which is an embryo of a genotype. Other nodes in the genotypes are chromosomes. The chromosomes contain system refinement options. The final solution is obtained after evolution process and mapping genotype to phenotype. Unlike existing genetic programming iterative improvement methodologies our algorithm starts from randomly generated system. Therefore the search space is not constrained by any initial condition. It is also easier for the algorithm to escape local minima of optimizing parameters.\",\"PeriodicalId\":72028,\"journal\":{\"name\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"volume\":\"1 1\",\"pages\":\"120-125\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0010391501200125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0010391501200125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic Programming based Iterative Improvement Algorithm for HW/SW Cosynthesis of Distributted Embedded Systems
In this work we present a novel genetic programming based iterative improvement approach for hardware/software cosynthesis of distributed embedded systems. The approach starts from a ready solution which is an embryo of a genotype. Other nodes in the genotypes are chromosomes. The chromosomes contain system refinement options. The final solution is obtained after evolution process and mapping genotype to phenotype. Unlike existing genetic programming iterative improvement methodologies our algorithm starts from randomly generated system. Therefore the search space is not constrained by any initial condition. It is also easier for the algorithm to escape local minima of optimizing parameters.
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