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In this paper a new class of hybridization strategies between GA and PSO is presented and validated. The Genetical Swarm Optimization (GSO) approach is presented here with respect with different test cases to prove its effectiveness. GSO is a hybrid evolutionary technique developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA), but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The here proposed class of hybrid algorithms is tested for various benchmark problems, analyzing different computational costs, and finally reporting some numerical results.