By borrowing the ideas of population from genetic algorithms, we introduce an adaptive random search in quasi_Monte Carlo method (AQMC) for global optimization.
借用遗传算法中种群的概念，介绍了一种解全局优化的拟蒙特卡罗自适应搜索算法。
Search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Random Walk Algorithms have been used extensively for global optimization.
搜索技术，诸如遗传算法，模拟退火算法，禁忌搜索和随机移动算法，已经广泛应用于全局优化。
Results show that that the search ability of TIGA algorithm, whose random degree is neither the highest nor the lowest, is the best among the tested sister algorithms.
由实验结果可知，随机性适中的TIGA算法对各测试函数的寻优能力最好。
Results show that that the search ability of TIGA algorithm, whose random degree is neither the highest nor the lowest, is the best among the tested sister algorithms.
由实验结果可知，随机性适中的TIGA算法对各测试函数的寻优能力最好。
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