Semiparametric Estimation of Distribution Algorithms for Continuous Optimization

Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to optimize continuous functions, such as the estimation of Gaussian network algorithms (EGNAs) which use Gaussian Bayesian networks (GBNs). However, this assumes a parametric density function, and, in GBNs, linear...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 28; H. 4; S. 1069 - 1083
Hauptverfasser:	Soloviev, Vicente P., Bielza, Concha, Larranaga, Pedro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.08.2024
Schlagworte:	Bayes methods Benchmarking Computational modeling continuous optimization Estimation estimation of distribution algorithms (EDAs) Kernel Probabilistic logic Probability distribution Runtime semiparametric Bayesian network (SPBN)
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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