An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise
Genetic and Evolutionary Computation Conference (GECCO), pp. 710–718
Abstract
The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is one of the most advanced algorithms in numerical black-box optimization. For noisy objective functions, several approaches were proposed to mitigate the noise, e.g., re-evaluations of the same solution or adapting the population size. In this paper, we devise a novel method to adaptively choose the optimal re-evaluation number for function values corrupted by additive Gaussian white noise. We derive a theoretical lower bound of the expected improvement achieved in one iteration of CMA-ES, given an estimation of the noise level and the Lipschitz constant of the function's gradient. Solving for the maximum of the lower bound, we obtain a simple expression of the optimal re-evaluation number. We experimentally compare our method to the state-of-the-art noise-handling methods for CMA-ES on a set of artificial test functions across various noise levels, optimization budgets, and dimensionality. Our method demonstrates significant advantages in terms of the probability of hitting near-optimal function values.
Authors 4
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
applied Quantum algorithms group, Leiden, Netherlands
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Leiden University · Honda (Germany)
Affiliation as printed
Honda Research Institute Europe GmbH, Frankfurt, Germany
Instituut-Lorentz, Leiden University, Frankfurt, Germany
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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