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Mateda-2.0: Estimation of Distribution Algorithms in MATLAB
(Journal of Statistical Software, UCLA Dept. Statistics, 2010-07)
This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based ...
An efficient implementation of kernel density estimation for multi-core and many-core architectures
(Sage, 2015-03-16)
Kernel density estimation (KDE) is a statistical technique used to estimate the probability density function of a sample set with unknown density function. It is considered a fundamental data-smoothing problem for use with ...
A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing
(IEEE, 2014-02-25)
The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator ...
Kernel density estimation in accelerators: Implementation and performance evaluation
(ACM, 2016-02-01)
Kernel density estimation (KDE) is a popular technique used to estimate the probability density function of a random variable. KDE is considered a fundamental data smoothing algorithm, and it is a common building block in ...
Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
(2015-01-01)
We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to ...