Analysis of selection hyper-heuristics for population-based meta-heuristics in real-valued dynamic optimization

Dynamic optimization problems provide a challenge in that optima have to be tracked as the environment changes. The complexity of a dynamic optimization problem is determined by the severity and frequency of changes, as well as the behavior of the values and trajectory of optima. While many efficient algorithms have been developed to solve these types of problems, the choice of the best algorithm is highly dependent on the type of change present in the environment. This paper analyses the ability of popular selection operators used in a hyper-heuristic framework to continuously select the most appropriate optimization method over time. Empirical studies examine the behavioral differences between various hyper-heuristic selection operators to better understand their mode of operation. The results show that these hyper-heuristic approaches can yield higher performance more consistently across difference types of environments.