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2010
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Gaussian Adaptation as a unifying framework for continuous black-box optimization and adaptive Monte Carlo sampling

9 years 6 months ago
Gaussian Adaptation as a unifying framework for continuous black-box optimization and adaptive Monte Carlo sampling
Abstract— We present a unifying framework for continuous optimization and sampling. This framework is based on Gaussian Adaptation (GaA), a search heuristic developed in the late 1960’s. It is a maximum-entropy method that shares several features with the (1+1)-variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The algorithm samples single candidate solutions from a multivariate normal distribution and continuously adapts the first and second moments. We present modifications that turn the algorithm into both a robust continuous black-box optimizer and, alternatively, an adaptive Random Walk Monte Carlo sampler. In black-box optimization, sample-point selection is controlled by a monotonically decreasing, fitness-dependent acceptance threshold. We provide general strategy parameter settings, stopping criteria, and restart mechanisms that render GaA quasi parameter free. We also introduce Metropolis GaA (M-GaA), where samplepoint selection is based on the Me...
Christian L. Müller, Ivo F. Sbalzarini
Added 13 Jan 2011
Updated 13 Jan 2011
Type Journal
Year 2010
Where CEC
Authors Christian L. Müller, Ivo F. Sbalzarini
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