Improving evolutionary algorithms by a new smoothing technique

Abstract

In this paper, a novel smoothing technique, which can be integrated into different optimization methods to improve their performance, is presented. At first, a new smoothing technique using a properly truncated Fourier series as the smoothing function is proposed. This smoothing function can eliminate many local minima and preserve the global minima. Thus it make the search of optimal solution more easier and faster. At second, this technique is integrated into a simple genetic algorithm to improve and demonstrate the efficiency of this technique. The simulation results also indicate the new smoothing technique can improve the simple genetic algorithm greatly. © Springer-Verlag Berlin Heidelberg 2004.