A tree-based radial basis function method for noisy parallel surrogate optimization
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Publication:6323998
arXiv1908.07980MaRDI QIDQ6323998
Author name not available (Why is that?)
Publication date: 21 August 2019
Abstract: Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems. In this work we develop a new parallel surrogate optimization algorithm (ProSRS), using a novel tree-based "zoom strategy" to improve the efficiency of the algorithm. We prove that if ProSRS is run for sufficiently long, with probability converging to one there will be at least one point among all the evaluations that will be arbitrarily close to the global minimum. We compare our algorithm to several state-of-the-art Bayesian optimization algorithms on a suite of standard benchmark functions and two real machine learning hyperparameter-tuning problems. We find that our algorithm not only achieves significantly faster optimization convergence, but is also 1-4 orders of magnitude cheaper in computational cost.
Has companion code repository: https://github.com/compdyn/ProSRS
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