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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT
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A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT

机译：基于概率编码的多序列对齐量子启发式遗传算法

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Quantum parallelism arises from the ability of a quantum memory register to exist inna superposition of base states. Since the number of possible base states is 2n, wherenn is the number of qubits in the quantum memory register, one operation on a quantumncomputer performs what an exponential number of operations on a classical computernperforms. The power of quantum algorithms comes from taking advantages ofnquantum parallelism. Quantum algorithms are exponentially faster than classical algorithms.nGenetic optimization algorithms are stochastic search algorithms which are usednto search large, nonlinear spaces where expert knowledge is lacking or difficult to encode.nQGMALIGN — a probabilistic coding based quantum-inspired genetic algorithm fornmultiple sequence alignment is presented. A quantum rotation gate as a mutation operatornis used to guide the quantum state evolution. Six genetic operators are designed onnthe coding basis to improve the solution during the evolutionary process. The experimentalnresults show that QGMALIGN can compete with the popular methods, such asnCLUSTALX and SAGA, and performs well on the presenting biological data. Moreover,nthe addition of genetic operators to the quantum-inspired algorithm lowers the cost ofnoverall running time
机译：量子并行性源自量子存储寄存器以基本状态的叠加形式存在的能力。由于可能的基本状态数是2n，其中nn是量子存储寄存器中的qubits数，因此量子计算机上的一项操作可以执行传统计算机上的指数级操作。量子算法的强大功能来自于利用n量子并行性。量子算法比经典算法快几倍。n遗传优化算法是随机搜索算法，用于搜索缺少或难以编码专家知识的大型非线性空间。nQGMALIGN —提出了一种基于概率编码的量子启发式遗传算法，用于多个序列比对。量子旋转门作为突变算子，用于指导量子态演化。在编码的基础上设计了六个遗传算子，以改进进化过程中的解。实验结果表明，QGMALIGN可以与流行的方法，如nCLUSTALX和SAGA竞争，并且在呈现生物学数据方面表现良好。此外，将遗传算子添加到量子启发式算法中可降低总体运行时间的成本

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