A Improved Evolutionary Programming for Global Optimization

In this paper, a Improved Evolutionary Programming (IEP) is proposed to solve global numerical optimization problems with continuous variables. In the methodology, the well-known Evolutionary Programming (EP) is used as a basic level search, which can give a good direction to the optimal global region. Then, a local search(LS) procedure is adopted as a fine tuning to determine the optimal solution. IEP methodology enhances the computational accuracy and accelerates convergence rate at the later period of the searching by adopting LS operator. The combination approach contributes to the local exploration and the global exploration of IEP. The proposed method is effectively applied to solve 12 benchmark problems. Results show a satisfactory improvement in comparison with the standard EP.