中文摘要：

最近，郭涛为解决功能优化问题在他的博士论文建议了一个随机的搜索算法。他把 subspace 搜索方法(一般多父母再结合策略) 与人口相结合爬山的方法。前者为全面状况保留全球搜索，并且后者保留算法的集中。郭的算法有许多优点，例如它的结构，它的结果的更高的精确性，它的应用的宽范围，和它的使用的坚韧性的简洁。在这份报纸，算法的初步的理论分析被给，一些数字实验被使用郭的算法表明理论结果做了。有为 MIMD 机器的不同颗粒度的三个异步的平行进化算法被 parallelizing 郭的算法设计。

英文摘要：

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace 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. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm.