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Multivariate Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for Mul-tivariate Gaussian based EDAs (MGEDAs), named Eigen Decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen(More)
—Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have(More)
— Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs). In this paper, we analyze continuous EDAs and show that they don't always work because of computation error: covariance matrix of Gaussian model can be ill-posed and Gaussian based EDAs using full covariance matrix will fail under specific conditions. It is a(More)
— Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian models as an example, EDAs based on single Gaussian distribution have good performance on solving simple unimodal functions and multimodal functions whose landscape has an obvious(More)
Water pipe failures can not only have a great impact on people's daily life but also cause significant waste of water which is an essential and precious resource to human beings. As a result, preventative maintenance for water pipes, particularly in urban-scale networks, is of great importance for a sustainable society. To achieve effective replacement and(More)
In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original(More)
—In this paper, we propose a new max-margin based discriminative feature learning method. Specifically, we aim at learning a low-dimensional feature representation, so as to maximize the global margin of the data and make the samples from the same class as close as possible. In order to enhance the robustness to noise, a l2,1 norm constraint is introduced(More)