A new framework for balancing both local and global optimizations in evolutionary algorithms

This paper presents a completely new approach to fulfill both local and global optimization goals simultaneously of the conventional evolutionary algorithm. The basis of the proposed framework is repeatedly alternating three different stages of evolution, each with its own objective and genetic operators. As the stages execute repeatedly, the conflicting goals of local optimization and global exploration are distributed gracefully across the generations of the different stages. The proposed system is compared with classical evolutionary programming (CEP), fast evolutionary programming (FEP) and improved fast evolutionary programming (IFEP) on a number of standard benchmark problems. The experimental results show that the new approach performs better optimization with a higher rate of convergence for most of the problems.