pyGPGO: Bayesian Optimization for Python

• DOI: 10.21105/joss.00431
• Publication Date: 2017-11-02T06:29:00Z
• Authors: José Jiménez; Josep Ginebra
• Keywords: Python; Bayesian Optimization

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