摘要

计算机软件测试数据自动采集过程中的退火和遗传算法研究

作者(年代):王瑞平，孙高飞

模拟退火算法的概念来源于统计力学中最优化和热平衡的有机结合。该算法模拟了受热后自然冷却的热平衡过程。在寻找最优解的过程中，该算法显示出很大的优势，它是一种寻找最优解的技术方法。在该算法中，极值应视为动态方程的函数。模拟退火算法虽然可以避免关注局部最优解，但仍存在计算量大、效率差等缺点。遗传算法(GA)模拟了生物学中的自然法则:适者生存。它运行一个 - global - optimized -算法，比模拟退火算法早出现几年。实际上，遗传算法是一组用于群体的算术算法。首先，有必要选择一组原始人口。然后，通过交叉和突变，产生一些新的种群。 This process goes on generation by generation, and always chooses the optimized ones to survive. As a result, the global optimized solution will be worked out. In this research, simulated annealing algorithm and genetic algorithm are combined to develop their biggest advantages to obtain the optimal solution. It is proved by test that this method is more suitable for seeking the optimal solution, especially, in automatic process of data collection in computer software test run. Its main advantages are high accuracy, convergence speed and high practical value, etc.