Bayesian optimization for parameter tuning in evolutionary algorithms

Advances in evolutionary computation have demonstrated that Evolutionary Algorithms (EAs) proposed in this area are a solid alternative for solving combinatorial and continuous optimization problems. Despite their success in innumerable real-world scenarios, EAs depend on a set of input parameters that characterize their performance and need to be adjusted. In fact, identifying and setting the most appropriate parameters for an EA is a complex task, which, in some cases, can be as difficult as the optimization problem at hand. Recently, parameter tuning has attracted the interest of the research community, designing and proposing techniques that (1) help the algorithm to perform to its best, and (2), indirectly, make fairer comparisons of different methods. In this manuscript, we propose a novel offline parameter tuning algorithm based on Bayesian Optimization, a sequential design strategy for global optimization. In order to illustrate the validity of the proposed method, we considered as a case of study the Hybrid Kernel EDA, an EA that is characterized by 6 parameters. We ran the algorithm with the parameters tuned by means of Bayesian Optimization, and compared the results with those obtained by setting the parameters by hand (using some prior knowledge). Experiments were carried out on a benchmark of 60 instances of the permutation flowshop scheduling problem. Experimental results show that, in general, Hybrid Kernel EDA obtains better results when using the parameters tuned by means of Bayesian Optimization.