Hyper-heuristics have a key role in solving challenging optimization problems. In this paper, we propose a new hyper-heuristic framework for continuous optimization problems. Numerical experiments conducted up to 750 dimensions for continuous benchmark problems prove the potential of the proposed framework. As an application, we present a clustering problem, where our proposed hyper-heuristic framework performs better than or similar to state-of-the-art clustering techniques.