{"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}
引用次数: 12
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.
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