Yuttana Suttasupa, Suppat Rungraungsilp, Suwat Pinyopan, Pravit Wungchusunti, P. Chongstitvatana
{"title":"A comparative study of linear encoding in Genetic Programming","authors":"Yuttana Suttasupa, Suppat Rungraungsilp, Suwat Pinyopan, Pravit Wungchusunti, P. Chongstitvatana","doi":"10.1109/ICTKE.2012.6152392","DOIUrl":null,"url":null,"abstract":"Genetic Programming is a widely used technique to solve many optimization problems. The original representation of a solution is a tree structure. To improve its search capability there are many proposals for encoding data structure of a solution of Genetic Programming as a linear code. However there are a few work in comparing between these proposals. This work presents a systematic way to compare three popular techniques for linear encoding in Genetic Programming. They are Linear Genetic Programming, Gene Expression Programming and Multi-Expression Programming. Ten problems in Symbolic Expressions are defined and are used as benchmarks to compare the effectiveness of these proposals against the baseline standard Genetic Programming. The metrics of comparison are the Success Rate and the absolute error. The discussion and comparison of the strength and weakness of each method are also presented.","PeriodicalId":235347,"journal":{"name":"2011 Ninth International Conference on ICT and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Ninth International Conference on ICT and Knowledge Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTKE.2012.6152392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Genetic Programming is a widely used technique to solve many optimization problems. The original representation of a solution is a tree structure. To improve its search capability there are many proposals for encoding data structure of a solution of Genetic Programming as a linear code. However there are a few work in comparing between these proposals. This work presents a systematic way to compare three popular techniques for linear encoding in Genetic Programming. They are Linear Genetic Programming, Gene Expression Programming and Multi-Expression Programming. Ten problems in Symbolic Expressions are defined and are used as benchmarks to compare the effectiveness of these proposals against the baseline standard Genetic Programming. The metrics of comparison are the Success Rate and the absolute error. The discussion and comparison of the strength and weakness of each method are also presented.
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