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    朱明放, 唐常杰, 代术成, 陈瑜, 乔少杰, 向勇. 基于中性突变的朴素基因表达式编程[J]. 计算机研究与发展, 2010, 47(2): 292-299.
    引用本文: 朱明放, 唐常杰, 代术成, 陈瑜, 乔少杰, 向勇. 基于中性突变的朴素基因表达式编程[J]. 计算机研究与发展, 2010, 47(2): 292-299.
    Zhu Mingfang, Tang Changjie, Dai Shucheng, Chen Yu, Qiao Shaojie, Xiang Yong. Nave Gene Expression Programming Based on Genetic Neutrality[J]. Journal of Computer Research and Development, 2010, 47(2): 292-299.
    Citation: Zhu Mingfang, Tang Changjie, Dai Shucheng, Chen Yu, Qiao Shaojie, Xiang Yong. Nave Gene Expression Programming Based on Genetic Neutrality[J]. Journal of Computer Research and Development, 2010, 47(2): 292-299.

    基于中性突变的朴素基因表达式编程

    Nave Gene Expression Programming Based on Genetic Neutrality

    • 摘要: 分子进化中性学说认为生物的进化主要是由中性突变决定的.基因表达式编程(GEP)是一种将基因型和表现型分离的新的进化模型,其突出表现在基因组存在不被表达的中性区.基于朴素基因表达式编程(NGEP)模型研究了NGEP中性区在进化中的作用.主要工作包括:1)进一步完善了基于完全树编码方案的NGEP模型的概念;2)分析了传统GEP和NGEP的基因中性区域特点,指出NGEP存在更自由灵活的中性区域;3)通过控制基因长度和基因数量,调控中性区的大小和数量,研究了NGEP和传统GEP的中性区域在进化中的特殊作用,验证了NGEP的有效性;4)实验表明,在存在相同适度的中性区域条件下,NGEP比传统GEP进化更有效,且NGEP的成功率随中性区域的增加不会发生剧烈变化.

       

      Abstract: The neutral theory of molecular evolution suggests that the accumulation of neutral mutations in the genome plays a vital role in evolutions. The genetic representation of gene expression programming (GEP), an artificial genotype and phenotype system, permits the existence of non-coding regions in the genome where neutral mutations can be accumulated. The authors introduce a concept named nave gene expression programming (NGEP) and analyze the effect in terms of neutral regions. NGEP uses the complete tree decoding method that causes more neutral regions than GEP. In order to explore the role of the genetic neutrality in NGEP, this paper makes the following contributions: 1)perfect the concept of nave gene expression programming, whose decoding method is based on complete tree; 2)analyze the characteristic of neutral regions in GEP and NGEP, and point out that NGEP has more free neutrality regions; 3)study and compare the specific role of genetic neutrality for both GEP and NGEP by controlling and adjusting the length and the number of genes and these non-coding regions, and tests the efficiency of NGEP; and 4)extensive experiments and comparisons show that NGEP is more efficient than traditional GEP in the case of similar gene redundancy, in particular, the success rate of NGEP does not change drastically with the growth of genetic neutrality.

       

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