Machine learning for determining the transition point in hybrid metaheuristics

High-level relay hybrids are among the most effective metaheuristics in multiple domains. However, the relay aspect of hybridization raises the problem of when to perform the transition from one algorithm to the next. This problem becomes more relevant in exploration-only exploitation-only hybrids, where each algorithm specializes in a specific task and performs rather poorly in the other. This paper presents a novel way of approaching the transition problem as a classification problem. Different classifiers are trained and tested on the MPS-CMAES hybrid; computational results are presented for the CEC'13 benchmark. The performance of the machine learning based hybrid confirms the effectiveness of the approach by achieving a significant improvement over the original hybrid.