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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Vydané v:	IEEE transactions on evolutionary computation Ročník 28; číslo 4; s. 1069 - 1083
Hlavní autori:	Soloviev, Vicente P., Bielza, Concha, Larranaga, Pedro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.08.2024
Predmet:	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
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