基于概率编码的多序列比对量子遗传算法。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI:10.1142/9781848162648_0002

Hongwei Huo, Qiao-Luan Xie, Xubang Shen, V. Stojkovic

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引用次数: 4

摘要

本文提出了一种结合遗传算法和量子算法的多序列比对量子遗传算法(QGMALIGN)。设计了一种表示多序列对齐的量子概率编码。利用量子旋转门作为突变算子来引导量子态演化。在编码的基础上设计了6个遗传算子，以改进进化过程中的解。利用量子力学中隐式并行性和状态叠加性的特点以及遗传算法的全局搜索能力，实现了高效的计算。参考BAliBASE2.0中一组著名的测试用例来评估QGMALIGN优化的效率。QGMALIGN结果与最流行的方法(CLUSTALX、SAGA、DIALIGN、SB_PIMA和QGMALIGN)结果进行了比较。QGMALIGN的结果表明，QGMALIGN在现有的生物学数据上表现良好。在量子算法中加入遗传算子，降低了总体运行时间成本。

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A probabilistic coding based quantum genetic algorithm for multiple sequence alignment.

This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The features of implicit parallelism and state superposition in quantum mechanics and the global search capability of the genetic algorithm are exploited to get efficient computation. A set of well known test cases from BAliBASE2.0 is used as reference to evaluate the efficiency of the QGMALIGN optimization. The QGMALIGN results have been compared with the most popular methods (CLUSTALX, SAGA, DIALIGN, SB_PIMA, and QGMALIGN) results. The QGMALIGN results show that QGMALIGN performs well on the presenting biological data. The addition of genetic operators to the quantum algorithm lowers the cost of overall running time.

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Computational systems bioinformatics. Computational Systems Bioinformatics Conference

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