pyGPGO: Bayesian Optimization for Python Article Swipe
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· 2017
· Open Access
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· DOI: https://doi.org/10.21105/joss.00431
· OA: W2766549992
Bayesian optimization has risen over the last few years as a very attractive method to optimize expensive to evaluate, black box, derivative-free and possibly noisy functions (Shahriari et al. 2016).This framework uses surrogate models, such as the likes of a Gaussian Process (Rasmussen and Williams 2004) which describe a prior belief over the possible objective functions in order to approximate them.The procedure itself is inherently sequential: our function is first evaluated a few times, a surrogate model is then fit with this information, which will later suggest the next point to be evaluated according to a predefined acquisition function.These strategies typically aim to balance exploitation and exploration, that is, areas where the posterior mean or variance of our surrogate model are high respectively.
