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 different types of changes may occur over time. Population‐based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems. 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 different characteristics. In this study, the performances of 35 single‐point‐search‐based selection hyper‐heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, produced by 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 to real‐valued optimization as well as being among the very few which address dynamic optimization issues using these techniques. The empirical results indicate that learning selection hyper‐heuristics incorporating compatible components can react to different types of changes in the environment and are capable of tracking them. This study shows the suitability of selection hyper‐heuristics as solvers in dynamic environments.