Volume 39 Issue 2
May  2017
Article Contents

ZHU Ming-fang, TANG Chang-jie, CHEN An-long, DAI Shu-cheng, YU Zhong-hua. Mining Compact Function Based on Na?ve Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 284-288,310. doi: 10.3969/j.issn.1001-0548.2010.02.028
Citation: ZHU Ming-fang, TANG Chang-jie, CHEN An-long, DAI Shu-cheng, YU Zhong-hua. Mining Compact Function Based on Na?ve Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 284-288,310. doi: 10.3969/j.issn.1001-0548.2010.02.028

Mining Compact Function Based on Na?ve Gene Expression Programming

doi: 10.3969/j.issn.1001-0548.2010.02.028
  • Received Date: 2008-07-25
  • Rev Recd Date: 2009-12-25
  • Publish Date: 2010-04-15
  • Gene Expression Programming (GEP) is a new member of evolutionary algorithm family, and it is an artificial genotype/phenotype system. Aiming to discover compact mathematical functions for function finding, this study analyzes the factors that greatly affect the efficiency of GEP, proposes the fitness function with pressure parameter, and implements a naïve gene expression programming (NGEP) for compact function mining tasks. Extensive experiments show that the proposed fitness function with compact pressure can automatically mine the compact functions as well as an alternative strategy to find compact results, and NGEP boosts the convergence speed by 21.7% than GEP, in addition, the results are more understandable than that are found by GEP. Compared with the evolution system without compact pressure, the average compact rate are 31.2% in GEP and 42.5% in NGEP, respectively, which shows that NGEP is easier to find compact results than GEP and the results are more comprehensive than traditional GEP.
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Mining Compact Function Based on Na?ve Gene Expression Programming

doi: 10.3969/j.issn.1001-0548.2010.02.028

Abstract: Gene Expression Programming (GEP) is a new member of evolutionary algorithm family, and it is an artificial genotype/phenotype system. Aiming to discover compact mathematical functions for function finding, this study analyzes the factors that greatly affect the efficiency of GEP, proposes the fitness function with pressure parameter, and implements a naïve gene expression programming (NGEP) for compact function mining tasks. Extensive experiments show that the proposed fitness function with compact pressure can automatically mine the compact functions as well as an alternative strategy to find compact results, and NGEP boosts the convergence speed by 21.7% than GEP, in addition, the results are more understandable than that are found by GEP. Compared with the evolution system without compact pressure, the average compact rate are 31.2% in GEP and 42.5% in NGEP, respectively, which shows that NGEP is easier to find compact results than GEP and the results are more comprehensive than traditional GEP.

ZHU Ming-fang, TANG Chang-jie, CHEN An-long, DAI Shu-cheng, YU Zhong-hua. Mining Compact Function Based on Na?ve Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 284-288,310. doi: 10.3969/j.issn.1001-0548.2010.02.028
Citation: ZHU Ming-fang, TANG Chang-jie, CHEN An-long, DAI Shu-cheng, YU Zhong-hua. Mining Compact Function Based on Na?ve Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 284-288,310. doi: 10.3969/j.issn.1001-0548.2010.02.028

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