The guided random search techniques, genetic algorithms and simulated annealing, are very promising strategies, and both techniques are analogs from physical and biological systems. Through genetic algorithms, the simulation of evolution for the purposes of parameter optimization has generally demonstrated itself to be a robust and rapid optimization technique. The simulated annealing algorithm often finds high quality candidate solutions. Limitations, however, occur in performance because optimization may take large numbers of iterations or final parameter values may be found that there are not at global minimum (or maximum) points. In this paper we propose a population-based search algorithm that combines the approaches from genetic algorithms and simulated annealing. The combined approach, called GASA, maintains a population of individuals over a period of generations. In the GASA technique, simulated annealing is used in choices regarding a subset of individuals to undergo crossover and mutation. We show that the GASA technique performs superior to a genetic algorithm on the Bohachevsky function, an objective function with m any local minima. The methodology and the test results on function optimization are given and compared with classical genetic algorithms.