Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm

Traditional Gaussian estimation of distribution algorithms (EDAs) are confronted with issues that the variable variances decrease fast and the main search direction tends to become perpendicular to the improvement direction of the fitness function, which reduces the search efficiency of Gaussian EDA...

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Veröffentlicht in:	Knowledge-based systems Jg. 146; S. 142 - 151
Hauptverfasser:	Ren, Zhigang, Liang, Yongsheng, Wang, Lin, Zhang, Aimin, Pang, Bei, Li, Biying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.04.2018 Elsevier Science Ltd
Schlagworte:	Algorithms Anisotropic adaptive variance scaling Anisotropy Convergence Estimation Evolutionary algorithms Fitness Gaussian estimation of distribution algorithm Genetic algorithms Global optimization Normal distribution Optimization Performance enhancement Premature convergence Scaling Search direction Searching Variance Variance analysis Search direction Premature convergence Anisotropic adaptive variance scaling Gaussian estimation of distribution algorithm
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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