Abstract
Evolutionary algorithms (EAs), in general, and genetic algorithms (GAs), in particular, are popular and efficient search metaheuristics, which have been applied for many complex optimization problems. At the same time, the performance of EAs depends on appropriate choice of the EA's structure and parameters. One of the ways to automate the EA design is to apply a hyper-heuristic approach. The hyper-heuristic is a high-level approach that can select and apply an appropriate low-level heuristic at each decision point. In this paper, we present a selection hyper-heuristic with online learning that is used to design and adaptively control an ensemble of many different genetic algorithms. The proposed approach combines concepts of the island model and cooperative and competitive coevolutions. The general method and some particular applications are discussed. The experimental results for a wide range of optimization problems are presented. The experiments show that the proposed approach outperforms its component metaheuristics on average. It also outperforms some state-of-the-art techniques. The main advantage of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.