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A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

  • Na Sun
  • Yong Lu
S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • 69 Downloads

Abstract

Genetic algorithm (GA) is an important and effective method to solve the optimization problem, which has been widely used in most practical applications. However, the premature convergence of GA has unexpected effect on the algorithm’s performance, the main reason is that the evolution of outstanding individuals multiply rapidly will lead to premature loss of population’s diversity. To solve the above problem, a method to qualify the population diversity and similarity between adjacent generations is proposed. Then, according to the evaluation of population diversity and the fitness of individual, the adaptive adjustment of crossover and mutation probability is realized. The results of several benchmark functions show that the proposed algorithm can search the optimal solution of almost all benchmark functions and effectively maintain the diversity of the population. Compared with the existing algorithms, it has greatly improved the convergence speed and the global optimal solution.

Keywords

Genetic algorithm Mutation mode Population diversity Self-adaptive genetic algorithm 

Notes

Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline (No. 10301-017004011501). And we wish to thank the anonymous reviewers who helped to improve the quality of the paper.

Compliance with ethical standards

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Information EngineeringMinzu University of ChinaBeijingChina

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