Hybrid genetic algorithms for global optimization problems

In the last two decades the field evolutionary computation has become a mainstream and several types of evolutionary algorithms are developed for solving optimization and search problems. Evolutionary algorithms (EAs) are mainly inspired from the biological process of evolution. They do not demand for any concrete information such as continuity or differentiability and other information related to the problems to be solved. Due to population based nature, EAs provide a set of solutions and share properties of adaptation through an iterative process. The steepest descent methods and Broyden-Fletcher-Goldfarb-Shanno (BFGS),Hill climbing local search are quite often used for exploitation purposes in order to improve the performance of the existing EAs. In this paper, We have employed the BFGS as an additional operator in the framework of Genetic Algorithm. The idea of add-in BFGS is to sharpen the search around local optima and to speeds up the search process of the suggested algorithm. We have used 24 benchmark functions which was designed for the special session of the 2005 IEEE-Congress on Evolutionary Computation (IEEE-CEC 06) to examine the performance of the suggested hybrid GA. The experimental results provided by HGBA are much competitive and promising as compared to the stand alone GA for dealing with most of the used test problems.