This paper puts forward the comparative study of the Classical Sine Cosine Algorithm and the newly introduced Hybrid Genetic Sine Cosine Algorithm. Though the existing literature proves that Sine Cosine Algorithm has sufficient capacity to explore the region of search space; how ever similar to other algorithms, it encounters a few complications like the stagnation of local optima, a less convergence rate with missing out of exact solutions. Thus, an advanced version of classical SCA is presented and is described as a Hybrid Genetic Sine Cosine Algorithm (HGSCA). In the proposed algorithm, the local state mechanism is hybridized with the global best state in the search equations to decide the region of search space around the global best position of a solution. In the search equation, global best is also combined with random steps to provide the statistics of the best position preserved in the memory of candidate solutions. A greedy selection mechanism and crossover with the personal best state reduce the overflow of diversity. An experimental setup that is established to execute both the algorithms on a benchmark Himmelblau function and the statistical analysis done proves the supremacy of HGSCA over Classical SCA.