This paper investigates the ability of evolutionary globallocal hybrid algorithms to handle dynamic fitness functions. Using a model where fitness functions vary in ruggedness as well as in whether changes occur gradually or abruptly, we evaluate the performance of Baldwinian and Lamarckian hybrid strategies and find them capable of locating and tracking a moving global optimum.