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An Improved Quantum Genetic Algorithm Based on Population Partition and Dynamic Probability Amplitude

  • Cheng Yao ShiEmail author
  • Zhao Cheng Xuan
  • Chao Yang
  • Yong Fei Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Aiming at the problem that quantum genetic algorithm is easy to fall into local optimization in the process of function optimization under the original framework, an improved quantum genetic algorithm using population partition method and dynamic inverse probability amplitude strategy is proposed. In the process of optimization, the improved algorithm utilizes the superior individuals in each generation of populations to construct a population genotype suitable for evolution through the individual binary form. On this basis, replace the inferior individuals in the population with a certain proportion, and then construct a number of similar individuals that tend to the genotype of the superior individual. Therefore, the algorithm can continuously improve the adaptability of the problem and gradually converge to the optimal solution. At the same time, for the premature or local optimization problem that the algorithm may be trapped, in the period of algorithm stagnation, the population diversity is enriched by resetting the genotype of some individuals and narrowing the search space by using the current optimal solution.

Keywords

Quantum genetic algorithm Population partition Dynamic probability amplitude 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cheng Yao Shi
    • 1
    Email author
  • Zhao Cheng Xuan
    • 1
  • Chao Yang
    • 1
  • Yong Fei Yang
    • 1
  1. 1.Department of Computer ScienceTianjin University of Technology and EducationTianjinChina

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