Results 251 to 260 of about 114,557 (285)

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.
openaire   +1 more source

Improving Evolution Strategies through Active Covariance Matrix Adaptation

2006 IEEE International Conference on Evolutionary Computation, 2006
This paper proposes a novel modification to the derandomised covariance matrix adaptation algorithm used in connection with evolution strategies. In existing variants of that algorithm, only information gathered from successful offspring candidate solutions contributes to the adaptation of the covariance matrix, while old information passively decays ...
G.A. Jastrebski, D.V. Arnold
openaire   +1 more source

Adaptive SVD algorithm for covariance matrix eigenstructure computation

International Conference on Acoustics, Speech, and Signal Processing, 2002
An adaptive algorithm is presented for covariance matrix eigenstructure computation based on the updated computation of the SVD (singular value decomposition) of a data matrix formed with the received data vectors appended as columns. Simulation results show that the algorithm is successful in tracking the eigenstructure of a time-varying covariance ...
W. Ferzali, J.G. Proakis
openaire   +1 more source

Evolving robust controller parameters using covariance matrix adaptation

Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010
In this paper, the advantages of introducing an additional amount of tests when evolving parameters for specific purposes is discussed. A set of optimal PID-controller parameters are sought for an exemplary system, which simulates a human-like robotic arm.
Gerulf K.M. Pedersen, Martin V. Butz
openaire   +1 more source

Evolutionary Bilevel Optimization Based on Covariance Matrix Adaptation

IEEE Transactions on Evolutionary Computation, 2019
Bilevel optimization refers to a challenging optimization problem which contains two levels of optimization problems. The task of bilevel optimization is to find the optimum of the upper-level problem, subject to the optimality of the corresponding lower-level problem.
Xiaoyu He, Yuren Zhou, Zefeng Chen
openaire   +1 more source

Expected-likelihood covariance matrix estimation for adaptive detection

IEEE International Radar Conference, 2005., 2005
We demonstrate that by adopting the new class of "expected-likelihood" (EL) covariance matrix estimates, instead of the traditional maximum-likelihood (ML) estimates, we can significantly enhance adaptive detection performance. These new estimates are found by searching within the properly parameterized class of admissible covariance matrices for the ...
Y.I. Abramovich, N.K. Spencer
openaire   +1 more source

Bayesian Multi-Class Covariance Matrix Filtering for Adaptive Environment Learning

2018 26th European Signal Processing Conference (EUSIPCO), 2018
Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic environment learning strategy to track both the covariance matrix and its class; the class represents a set of structured covariance matrices.
BRACA, ALESSANDRA, Aubry A., Millefiori L. M., De Maio A., Marano S.   +4 more
openaire   +3 more sources

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

2008
The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or ...
Hans-Georg Beyer, Bernhard Sendhoff
openaire   +1 more source

Adaptive Detection after Covariance Matrix Classification

2022
Jun Liu, Danilo Orlando, Chengpeng Hao, Weijian Liu   +3 more
openaire   +1 more source

CoMABO: Covariance Matrix Adaptation for Bayesian Optimization

2023 IEEE International Conference on Big Data (BigData), 2023
Hsiang-Yu Ku, Che-Rung Lee
openaire   +1 more source