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In this paper, an effective adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) that is applied to cope with mixed-integer nonlinear programming problems. The proposed method synthesized the merits of both genetic algorithm and simulated annealing. Adaptive mechanisms are also included to make the evolutionary scheme active and result in improving the hill-climbing ability and the convergence speed. The performances of this proposed algorithm are demonstrated in several large parameter optimization functions. Due to their versatile characteristics, these examples are suitable to test the ability of the proposed algorithm. The results of this novel hybrid algorithm under different population sizes and frozen numbers were discussed and appropriate parametric combinations of both parameters were also suggested in this paper. ARSAGA also shows excellent performances in large parameter mixed-integer optimization problems.