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Prediction of the RNA Secondary Structure Using a Multi-Population Assisted Quantum Genetic Algorithm.
Human Heredity ( IF 1.8 ) Pub Date : 2019-08-28 , DOI: 10.1159/000501480
Sha Shi 1 , Xin-Li Zhang 2 , Xian-Li Zhao 3 , Le Yang 4 , Wei Du 5 , Yun-Jiang Wang 6
Affiliation  

Quantum-inspired genetic algorithms (QGAs) were recently introduced for the prediction of RNA secondary structures, and they showed some superiority over the existing popular strategies. In this paper, for RNA secondary structure prediction, we introduce a new QGA named multi-population assisted quantum genetic algorithm (MAQGA). In contrast to the existing QGAs, our strategy involves multi-populations which evolve together in a cooperative way in each iteration, and the genetic exchange between various populations is performed by an operator transfer operation. The numerical results show that the performances of existing genetic algorithms (evolutionary algorithms [EAs]), including traditional EAs and QGAs, can be significantly improved by using our approach. Moreover, for RNA sequences with middle-short length, the MAQGA improves even this state-of-the-art software in terms of both prediction accuracy and sensitivity.

中文翻译:

使用多种群辅助量子遗传算法预测RNA二级结构。

最近引入了量子启发遗传算法(QGA)来预测RNA二级结构,与现有的流行策略相比,它们显示出一些优势。在本文中,对于RNA二级结构的预测,我们引入了一种新的QGA,称为多种群辅助量子遗传算法(MAQGA)。与现有的QGA相比,我们的策略涉及在每次迭代中以协同方式共同进化的多种群,并且通过操作员转移操作来进行不同种群之间的遗传交换。数值结果表明,使用我们的方法可以显着改善包括传统EA和QGA在内的现有遗传算法(进化算法[EA])的性能。此外,对于中等长度的RNA序列,
更新日期:2019-11-01
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