This article presents a novel optimization technique hybridizing the concepts of Genetic Algorithm (GA) and Lbest Particle Swarm Optimization (Lbest PSO). A new topology, namely 'Dynamically Varying Sub-swarm' has been incorporated in the search process and some selected crossover and mutation techniques have been used for generation updating. This novel hybridized approach simultaneously ensures a robust search process, a quick convergence and a wide variety of real life applications. Simulations performed over various benchmark functions with the proposed method have been compared with other existing strong algorithms. Experimental results support the claim of proficiency of our algorithm over other existing techniques in terms of robustness, fast convergence and, most importantly its optimal search behavior.