Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study

The parameter setting of an algorithm that will result in optimal performance differs across problem instance domains. Users spend a lot of time tuning algorithms for their specific problem domain, and this time could be saved by an automatic approach for parameter tuning. s paper, we present a system that recommends the parameter configuration of an algorithm that solves a problem, conditioned by the particular features of the current problem instance to be solved. The proposed system is based on a basic adjustment model designed by authors (Pavon, R., Díaz, F., & Luzón, V. (2008). A model for parameter setting based on Bayesian networks. Engineering Applications of Artificial Intelligence, 21(1), 14–25) in which starting from experimental results concerning the search for solutions to several instances of the problem, a Bayesian network (BN) is induced and tries to infer the best configuration for the algorithm used. r, taking into account that the optimal parameter configuration may differ considerably across problem instances of a specific domain, the present work extends the former incorporating additional information about problem instances and using the case-based reasoning (CBR) methodology as the framework integrator for the different instances from the same problem, where each problem instance deals with a specific BN. In this way, the system will automatically recommend a parameter configuration for a given algorithm according to the characteristics of the problem instance at hand and past experience of similar instances. example, we empirically evaluate our Bayesian CBR system to tune a genetic algorithm for solving the root identification problem. The experimental results demonstrate the validity of the model proposed.