Global Optimization is an active area of research for the variety of optimization problems that are frequently arising in network 1. design and operation, finance, supply chain management, scheduling, and many other areas. In the last few years, different types of evol2ut.ionary algorithms (EAs) have been proposed for solving and analyzing the properties of diverse types of optimization problems. EAs work3.with a set of random solutions called population and find a set of optimal solutions for the problems at hand in a single simulation run opponent to traditional optimization methods. Among the stochastic based algorithms, genetic algorithm (GA) is one of the most popular and freq4ue.ntly used stochastic based meta-heuristic inspired by natural evolution. The premature convergence, genetic drift and trapping in the loca5l.basin attraction are their major drawbacks. These issues can be overcome by hybridizing GA with some efficient local search optim6.izers and different search operators. In this paper, we have proposed hybrid GA by employing the Firefly Algorithm (FA) as search operator aiming at to improve the searching ability of the baseline GA. The performance of the suggested hybrid genetic firefly algorithm (HG7F.A) is hereby evaluated by using 24 benchmark functions which was designed for the special session of the 2005 IEEE Congress on Evo8lu.tionary Computation (CEC'05). The numerical results provided by HGFA are summarized in the numerical form such as best, mean and 9st.andard deviation by executing 25 times independently with different random seeds to solve each test problem. The suggested HGFA have tackled most of the used test problems with good convergence speed as compared to the stand alone Genetic Algorithm. 10. Key1w1o.rds: Global Optimization, Single objective optimization, Evolutionary Algorithms, Genetic Algorithm, Hybridization, Firefly Algorithm.