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Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model

机译:使用低分辨率模型的蛋白质结构预测遗传算法中的孪生去除

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This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.
机译:本文介绍了双胞胎的影响以及将其应用于有效构象搜索时从遗传算法(GA)群体中移除的措施。结论表明,在研究低分辨率模型中复杂的从头算蛋白质结构预测(PSP)问题的解决方案时,GA的双胞胎去除策略可显着提高性能。如果不去除双胞胎,GA交换和突变操作将变得无效,因为世代失去了产生显着差异的能力,这可能导致解决方案停滞。该论文放宽了去除策略中对孪生染色体的定义,使其不仅涵盖了GA种群中相同的染色体,而且还包含了高度相关的染色体,经验结果始终显示出解决PSP问题的显着改进。

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