Focusing on continuous EDAs with Gaussian models, this paper proposes a novel probability density estimator which can adaptively enlarge the variances and thus ...
Abstract—Estimation of distribution algorithm (EDA) is a kind of typical model-based evolutionary algorithm (EA). Although possessing competitive advantages ...
Focusing on continuous EDAs with Gaussian models, this paper proposes a novel probability density estimator which can adaptively enlarge the variances and thus ...
... In this method, the difference between the sampled population and the prediction of the probabilistic model is first measured, and the scaling factor on the ...
For the estimated probability density, a reflecting sampling strategy which can further improve the search efficiency is put forward. With these two algorithmic ...
Bibliographic details on Enhance continuous estimation of distribution algorithm by variance enlargement and reflecting sampling.
We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs.
In this article, two novel density ensembles methods - the resampling method and the subspaces method - are proposed for enhancing existing continuous ...
Missing: variance reflecting
Jul 19, 2022 · Enhance Continuous Estimation of Distribution Algorithm by Variance Enlargement and Reflecting Sampling. IEEE Congress on Evolutionary ...
May 3, 2020 · Abstract. The local pivotal method (LPM) is a successful sampling method for taking well-spread samples from discrete populations.