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Bayesian Optimization is the state of the art technique for the optimizationof black boxes, i.e., functions where we do not have access to their analyticalexpression nor its gradients, they are expensive to evaluate and its evaluationis noisy. The most popular application of bayesian optimization is theautomatic hyperparameter tuning of machine learning algorithms, where we obtainthe best configuration of machine learning algorithms by optimizing theestimation of the generalization error of these algorithms. Despite beingapplied with success, bayesian optimization methodologies also havehyperparameters that need to be configured such as the probabilistic surrogatemodel or the acquisition function used. A bad decision over the configurationof these hyperparameters implies obtaining bad quality results. Typically,these hyperparameters are tuned by making assumptions of the objective functionthat we want to evaluate but there are scenarios where we do not have any priorinformation about the objective function. In this paper, we propose a firstattempt over automatic bayesian optimization by exploring several heuristicsthat automatically tune the acquisition function of bayesian optimization. Weillustrate the effectiveness of these heurisitcs in a set of benchmark problemsand a hyperparameter tuning problem of a machine learning algorithm.

Towards Automatic Bayesian Optimization: A first step involving acquisition functions