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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Popis
Shrnutí: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 dependencies between variables. Furthermore, the EGNA baseline learns a GBN at each iteration based on the best individuals in the last iteration, which may lead to local optimum convergence or large variance between solutions across multiple independent runs of the algorithm. In this work, we propose a semiparametric EDA in which the restriction of assuming Gaussianity in the variables is relaxed using semiparametric Bayesian networks (SPBNs), in which nodes estimated by kernels coexist with nodes that assume Gaussianity, and the algorithm itself is able to determine where to use each type of node. Additionally, our approach takes into account information from several past iterations to learn the SPBN from which the new solutions are sampled in each iteration. The empirical results show that semiparametric EDAs are a useful tool for continuous scenarios compared to different kinds of EDAs and other optimization techniques in continuous environments.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3290670