Derivative Based Hybrid Genetic Algorithm: A Preliminary Experimental Results

. Global Optimization has become an important branch of mathematical analysis and numerical analysis in the recent years. Practical example of the optimization problems including the design and optimization of electrical circuit in electrical engineering, object packing problems, the Gibbs free energy in chemical engineering and the Protein structure prediction problems. Genetic algorithm (GA) is one of the most popular population based and stochastic nature based techniques in the field of evolutionary computation (EC). GA mimics the process of natural evolution and provides the maximum or minimum objective function value in a single simulation run unlike traditional optimization methods. This paradigm has great ability to efficiently locate the region in which the global optimum of the test problems exists. However, sometime, it has difficulties and spends much time to find the exact local optimum in the search space of the given test suites and complicated real world optimization problems. In such a situation, local search (LS) techniques are very good tools to handle these issues by incorporating them in the framework of evolutionary algorithms in order to improve further their global search process. In this paper, we have incorporated the Broyden-Fletcher-Goldfarb-Shanno (BFGS) as local search optimizer in GA framework with a hope to alleviate the issues related to optimality and convergence of the original GA. The performance of the suggested hybrid GA (HGA) have been examined by selecting eight test problems from the widely used benchmark functions. The suggested HGA have shown promising results for dealing with most of the test problems compared to simple GA by implementing them in a Matlab 2013 environment.&nbsp