Volume 39 Issue 2
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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2010  >  39(2): 279-283

LI Tai-yong, TANG Chang-jie, WU Jiang, QIAO Shao-jie, JIANG Yue, CHEN Yu. Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 279-283. doi: 10.3969/j.issn.1001-0548.2010.02.027
Citation: LI Tai-yong, TANG Chang-jie, WU Jiang, QIAO Shao-jie, JIANG Yue, CHEN Yu. Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 279-283. doi: 10.3969/j.issn.1001-0548.2010.02.027
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Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming

doi: 10.3969/j.issn.1001-0548.2010.02.027
  • 1.

    School of Economic Information Engineering,Southwestern University of Finance and Economics Chengdu 610074;

  • 2.

    School of Computer Science,Sichuan University Chengdu 610065;

  • 3.

    School of Information Science and Technology,Southwest Jiaotong University Chengdu 610031

  • Received Date: 2008-08-29
  • Rev Recd Date: 2009-12-18
  • Publish Date: 2010-04-15
  • To cope with the problem of controlling population diversity in gene expression programming (GEP), an adaptive population diversity tuning algorithm is proposed. A weighted measurement for population diversity is designed. The impact in terms of selection, crossover, and mutation operators on population diversity is analyzed in detail. A diversity algorithm for initial population (DAIP) maximizing the initial population diversity is proposed as well. Aiming to appropriately maintain the population diversity and achieve high evolution efficiency, adaptive crossover and mutation operations are developed and an adaptive population diversity tuning algorithm (APDTA) is developed. Experiments show that APDTA is efficient and effective.
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Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming

doi: 10.3969/j.issn.1001-0548.2010.02.027
  • 1. School of Economic Information Engineering,Southwestern University of Finance and Economics Chengdu 610074;
  • 2. School of Computer Science,Sichuan University Chengdu 610065;
  • 3. School of Information Science and Technology,Southwest Jiaotong University Chengdu 610031
  • Received Date: 2008-08-29
  • Rev Recd Date: 2009-12-18
  • Publish Date: 2010-04-15

Abstract: To cope with the problem of controlling population diversity in gene expression programming (GEP), an adaptive population diversity tuning algorithm is proposed. A weighted measurement for population diversity is designed. The impact in terms of selection, crossover, and mutation operators on population diversity is analyzed in detail. A diversity algorithm for initial population (DAIP) maximizing the initial population diversity is proposed as well. Aiming to appropriately maintain the population diversity and achieve high evolution efficiency, adaptive crossover and mutation operations are developed and an adaptive population diversity tuning algorithm (APDTA) is developed. Experiments show that APDTA is efficient and effective.

LI Tai-yong, TANG Chang-jie, WU Jiang, QIAO Shao-jie, JIANG Yue, CHEN Yu. Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 279-283. doi: 10.3969/j.issn.1001-0548.2010.02.027
Citation: LI Tai-yong, TANG Chang-jie, WU Jiang, QIAO Shao-jie, JIANG Yue, CHEN Yu. Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 279-283. doi: 10.3969/j.issn.1001-0548.2010.02.027
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