Genetic algorithms and simulated annealing are leading methods of search and optimization. This paper proposes an efficient hybrid genetic algorithm named ASAGA (Adaptive Simulated Annealing Genetic Algorithm). Genetic algorithms are global search techniques for optimization. However, they are poor at hill-climbing. Simulated annealing has the ability of probabilistic hill-climbing. Therefore, the two techniques are combined here to produce an adaptive algorithm that has the merits of both genetic algorithms and simulated annealing, by introducing a mutation operator like simulated annealing and an adaptive cooling schedule. The validity and the efficiency of the proposed algorithm are shown by an example involving system identification.