{"title":"基于遗传算法的电路进化设计优化算法","authors":"Xuejun Song, Yanli Cui, Aiting Li","doi":"10.1109/ISCID.2012.91","DOIUrl":null,"url":null,"abstract":"For the convergence speed and scale bottlenecks of evolutionary design of circuits, the paper explores a new evolutionary method on the basis of genetic algorithm. Several optimization methods including fitness sharing, exponential weighting, double selection population, \"Queen bee\" mating, module crossover and optimal solution set are proposed to improve genetic algorithm. the new algorithm improved fitness evaluation method and genetic strategies. the experiment shows that the new evolutionary algorithm accelerates evolution convergence greatly, improves the adaptability effectively and expands the scale of evolved circuit obviously.","PeriodicalId":246432,"journal":{"name":"2012 Fifth International Symposium on Computational Intelligence and Design","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Optimization Algorithm of Evolutionary Design of Circuits Based on Genetic Algorithm\",\"authors\":\"Xuejun Song, Yanli Cui, Aiting Li\",\"doi\":\"10.1109/ISCID.2012.91\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the convergence speed and scale bottlenecks of evolutionary design of circuits, the paper explores a new evolutionary method on the basis of genetic algorithm. Several optimization methods including fitness sharing, exponential weighting, double selection population, \\\"Queen bee\\\" mating, module crossover and optimal solution set are proposed to improve genetic algorithm. the new algorithm improved fitness evaluation method and genetic strategies. the experiment shows that the new evolutionary algorithm accelerates evolution convergence greatly, improves the adaptability effectively and expands the scale of evolved circuit obviously.\",\"PeriodicalId\":246432,\"journal\":{\"name\":\"2012 Fifth International Symposium on Computational Intelligence and Design\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fifth International Symposium on Computational Intelligence and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID.2012.91\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fifth International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2012.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization Algorithm of Evolutionary Design of Circuits Based on Genetic Algorithm
For the convergence speed and scale bottlenecks of evolutionary design of circuits, the paper explores a new evolutionary method on the basis of genetic algorithm. Several optimization methods including fitness sharing, exponential weighting, double selection population, "Queen bee" mating, module crossover and optimal solution set are proposed to improve genetic algorithm. the new algorithm improved fitness evaluation method and genetic strategies. the experiment shows that the new evolutionary algorithm accelerates evolution convergence greatly, improves the adaptability effectively and expands the scale of evolved circuit obviously.