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Quantum novel genetic algorithm based on parallel subpopulation computing and its application

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Abstract

A quantum novel genetic algorithm based on subpopulation parallel computing is presented, where quantum coding and rotation angle are improved to inspire more efficient genetic computing methods. In the algorithm, each axis of the solution space is divided into k parts, the individual (or chromosome) from each different subspace being coded differently, and the probability amplitude of each quantum bit or Q-bit is regarded as two paratactic genes. The basic quantum computing theory and classical quantum genetic algorithm are briefly introduced before a novel algorithm is presented for the function optimum and PID problem. Through a comparison between the novel algorithm and the classical counterpart, it is shown that the quantum inspired genetic algorithm performs better on running speed and optimization capability.

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Authors and Affiliations

  1. College of Information Engineering, East China Jiao Tong University, Nanchang, 330013, Jiangxi, People’s Republic of China

    Rigui Zhou & Jian Cao

  2. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, Hunan, People’s Republic of China

    Rigui Zhou

Authors
  1. Rigui Zhou
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  2. Jian Cao
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Correspondence to Rigui Zhou.

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Zhou, R., Cao, J. Quantum novel genetic algorithm based on parallel subpopulation computing and its application. Artif Intell Rev 41, 359–371 (2014). https://doi.org/10.1007/s10462-012-9312-8

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  • DOI: https://doi.org/10.1007/s10462-012-9312-8

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