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Sparse Covariance Matrix Adaptation Techniques for Evolution Strategies
2015Evolution strategies are variants of evolutionary algorithms. In contrast to genetic algorithms, their search process depends strongly on mutation. Since the search space is often continuous, evolution strategies use a multivariate normal distribution as search distribution. This necessitates the tuning and adaptation of the covariance matrix.
Silja Meyer-Nieberg, Erik Kropat
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Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –
2008The 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
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CoMABO: Covariance Matrix Adaptation for Bayesian Optimization
2023 IEEE International Conference on Big Data (BigData), 2023Hsiang-Yu Ku, Che-Rung Lee
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Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation
2020We aim to observe differences between surrogate model assisted covariance matrix adaptation evolution strategies applied to simple test problems. We propose a simple Gaussian process assisted strategy as a baseline. The performance of the algorithm is compared with those of several related strategies using families of parameterized, unimodal test ...
Lauchlan Toal, Dirk V. Arnold
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Adaptive regularization of high-dimensional Toeplitz covariance matrix
Statistics & Probability LettersWe propose a Toeplitz covariance estimator using a nonconvex SCAD penalty and smoothing term. An efficient ADMM algorithm with theoretical convergences is investigated. Simulations and real data show its effectiveness for high-dimensional data.
Deliang Dai +3 more
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On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes
2012We evaluate the performance of several gradient-free variable-metric continuous optimization schemes on a specific set of quadratic functions. We revisit a randomized Hessian approximation scheme (D. Leventhal and A. S. Lewis. Randomized Hessian estimation and directional search, 2011), discuss its theoretical underpinnings, and introduce a novel ...
Sebastian U. Stich, Christian L. Müller
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A Dynamic Partial Update for Covariance Matrix Adaptation
Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023Hiroki Shimizu, Masashi Toyoda
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Multistage Covariance Matrix Adaptation with Differential Evolution for Constrained Optimization
2012Single Objective minimizations often involve simultaneous satisfaction of a number of conditions, known as constraints. MCMADE proposes a two-stage algorithm having an initial CMA or Covariance Matrix Adaptation phase and a subsequent Differential Evolution strategy in the second phase. The two phases are synchronized using a stagnate parameter.
Shantanab Debchoudhury +2 more
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Theory and application of covariance matrix tapers for robust adaptive beamforming
IEEE Transactions on Signal Processing, 1999We unify several seemingly disparate approaches to robust adaptive beamforming through the introduction of the concept of a "covariance matrix taper (CMT)". This is accomplished by recognizing that an important class of adapted pattern modification techniques are realized by the application of a conformal matrix "taper" to the original sample ...
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A knowledge-based differential covariance matrix adaptation cooperative algorithm
Expert Systems With Applications, 2021Fuqing Zhao, Yang Zuo
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