  Bayesian Optimization is the state of the art technique for the optimization
of black boxes, i.e., functions where we do not have access to their analytical
expression nor its gradients, they are expensive to evaluate and its evaluation
is noisy. The most popular application of bayesian optimization is the
automatic hyperparameter tuning of machine learning algorithms, where we obtain
the best configuration of machine learning algorithms by optimizing the
estimation of the generalization error of these algorithms. Despite being
applied with success, bayesian optimization methodologies also have
hyperparameters that need to be configured such as the probabilistic surrogate
model or the acquisition function used. A bad decision over the configuration
of these hyperparameters implies obtaining bad quality results. Typically,
these hyperparameters are tuned by making assumptions of the objective function
that we want to evaluate but there are scenarios where we do not have any prior
information about the objective function. In this paper, we propose a first
attempt over automatic bayesian optimization by exploring several heuristics
that automatically tune the acquisition function of bayesian optimization. We
illustrate the effectiveness of these heurisitcs in a set of benchmark problems
and a hyperparameter tuning problem of a machine learning algorithm.
