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Neural Computing and Applications

Erschienen in:

05.04.2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

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

verfasst von: Na Sun, Yong Lu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2019

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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.

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Titel: A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity
verfasst von: Na Sun
Yong Lu
Publikationsdatum: 05.04.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3438-9

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