Differential Evolution (DE) is a simple and surprisingly efficient algorithm for global optimization over continuous spaces. It has reportedly outperformed many versions of EA and other search heuristics when tested over both benchmark and real world problems. However the performance of DE deteriorates severely if the fitness function is noisy and continuously changing. In this paper we propose an improved DE scheme which can efficiently track the global optima of a noisy function. The scheme performs better than the classical DE, PSO, and an EA over a set of benchmark noisy problems.