Results 211 to 220 of about 17,544 (260)

Utilizing statistical analysis for motion imagination classification in brain-computer interface systems. [PDF]

PLoS One
Li Y, Zhang J.
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An Improved Cubature Kalman Filter for GNSS-Denied and System-Noise-Varying INS/GNSS Navigation. [PDF]

Micromachines (Basel)
Liu D, Chen X, Cui B.
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A Comparative Evaluation of Molecular Connectivity and Covariance Approaches

Reed MB, Graf S, Murgaš M, Eggerstorfer B, Milz C, Falb P, Briem E, Mayerweg A, Schlosser G, Nics L, Godbersen GM, Rasul S, Hacker M, Hahn A, Lanzenberger R.   +14 more
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Toward a Matrix-Free Covariance Matrix Adaptation Evolution Strategy

IEEE Transactions on Evolutionary Computation, 2020
In this paper, we discuss a method for generating new individuals such that their mean vector and the covariance matrix are defined by formulas analogous to the covariance matrix adaptation evolution strategy (CMA-ES). In contrast to CMA-ES, which generates new individuals using multivariate Gaussian distribution with an explicitly defined covariance ...
Dariusz Jagodziński, Jaroslaw Arabas
exaly   +2 more sources

Simplify Your Covariance Matrix Adaptation Evolution Strategy

IEEE Transactions on Evolutionary Computation, 2017
The standard covariance matrix adaptation evolution strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank-1 update of the covariance matrix. In this paper, it is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance ...
Bernhard Sendhoff, Hans-Georg Beyer
exaly   +2 more sources

Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits

IEEE Transactions on Neural Networks and Learning Systems, 2019
Upper confidence bound (UCB) is a successful multiarmed bandit for regret minimization. The covariance matrix adaptation (CMA) for Pareto UCB (CMA-PUCB) algorithm considers stochastic reward vectors with correlated objectives. We upper bound the cumulative pseudoregret of pulling suboptimal arms for the CMA-PUCB algorithm to logarithmic number of arms ...
Madalina Drugan
exaly   +3 more sources

Adapting the Covariance Matrix in Evolution Strategies

Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, 2014
Evolution strategies belong to the best performing modern metaheuristics for continuous optimization. This paper addresses the covariance matrix adaptation in evolution strategies which is central to the algorithm. Nearly all approaches so far consider the sample covariance matrix as one of the main factors for the adaptation.
Silja Meyer-Nieberg, Erik Kropat
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Covariance Matrix Adaptation for Multi-objective Optimization

Evolutionary Computation, 2007
The covariancematrix adaptation evolution strategy (CMA-ES) is one of themost powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on
Christian Igel, Nikolaus Hansen, Stefan Roth 0003   +2 more
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Enhanced Covariance Matrix Estimators in Adaptive Beamforming

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is ...
Richard Abrahamsson, Yngve Selén, Petre Stoica   +2 more
openaire   +1 more source