Summary: Guo Tao proposed a stochastic search algorithm in bis PhD thesis for solving function optimization problems. He combined the sub-space search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. In this paper the characteristics of the algorithm are given and some numerical experiments have been done for demostrating the efficiency of the algorithm. Guo's algorithm has been parallelized as asynchronous parallel algorithms for suiting different parallel and distributed computing environments. The Bump problem as a numerical example is solved by a superparallel-computer and some best results are obtained.