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Recent developments on identifying the community structures in real-world networks have established that the community structure may be either disjoint community set (DCS) or overlapping community set (OCS), showing high resemblance with each other. Still, given a network, researchers mostly followed distinct approaches to achieve optimal solutions, either for DCS or for OCS. Moreover, prior knowledge of community structure is needed to select the appropriate class of algorithms, since one cannot produce the optimal solution for the other. In this article, a comprehensive two-phase approach based on genetic algorithm (GA) is proposed that can be applied to any small-world network to generate the DCS and the OCS very fast without any prior knowledge of the community structure. In the first phase, an upper bound on the mean path length of a community is applied, relative to the equivalent Erdős-Rényi (E-R) random graph that expedites the convergence significantly. In the second phase, the search space is reduced considerably, by selecting a smaller subset of boundary nodes of the DCS generated in the first phase, to be manipulated probabilistically. To the best of our knowledge, we are the first to consider the mean path length of the community as a key parameter for finding the good quality communities at the earliest. Experimental study on six synthetic networks and five real-world networks shows that the proposed approach not only outperforms the state-of-the-art algorithms in terms of quality and scalability, but its parallel implementation also improves the speedup significantly.