{"title":"演化微分方程与发育线性遗传规划及表观遗传爬坡","authors":"W. L. Cava, L. Spector, K. Danai, M. Lackner","doi":"10.1145/2598394.2598491","DOIUrl":null,"url":null,"abstract":"This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing\",\"authors\":\"W. L. Cava, L. Spector, K. Danai, M. Lackner\",\"doi\":\"10.1145/2598394.2598491\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.\",\"PeriodicalId\":298232,\"journal\":{\"name\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2598394.2598491\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2598394.2598491","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing
This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.
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