Selection hyper-heuristics in dynamic environments

Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real world problems are dynamic in nature, where dierent types of changes may occur over time. Population based approaches, such as evolutionary algorithms are frequently used in dynamic environments. Selection hyper-heuristics are highly adaptive search methodologies that aim to raise the level of generality by providing solutions to a diverse set of problems having dierent characteristics. In this study, thirty-ve single point search based selection hyper-heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, generated using the Moving Peaks Benchmark generator. Even though there are many successful applications of selection hyper-heuristics to discrete optimization problems, to the best of our knowledge, this study is one of the initial applications of selection hyper-heuristics for real-valued optimization as well as being among the very few which address dynamic optimization issues with these techniques. The empirical results indicate that selection hyper-heuristics with compatible components can react to dierent types of changes in the environment and are capable of tracking them. This shows the suitability of selection hyper-heuristics as solvers in dynamic environments.