A Novel Genetic Algorithm for Global Optimization

This paper presents a novel genetic algorithm for globally solving un-constraint optimization problem. In this algorithm, a new real coded crossover operator is proposed firstly. Furthermore, for improving the convergence speed and the searching ability of our algorithm, the good point set theory ra...

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Bibliographic Details
Published in:Acta Mathematicae Applicatae Sinica 2020-03, Vol.36 (2), p.482-491
Main Authors: Wang, Chun-feng, Liu, Kui, Shen, Pei-ping
Format: Article
Language:English
Subjects:
Applications of Mathematics
Convergence
Crossovers
Genetic algorithms
Global optimization
Iterative methods
Math Applications in Computer Science
Mathematical analysis
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Set theory
Theoretical
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Summary:This paper presents a novel genetic algorithm for globally solving un-constraint optimization problem. In this algorithm, a new real coded crossover operator is proposed firstly. Furthermore, for improving the convergence speed and the searching ability of our algorithm, the good point set theory rather than random selection is used to generate the initial population, and the chaotic search operator is adopted in the best solution of the current iteration. The experimental results tested on numerical benchmark functions show that this algorithm has excellent solution quality and convergence characteristics, and performs better than some algorithms.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-020-0930-7