机构地区: 暨南大学
出 处: 《微计算机信息》 2007年第03X期205-206,共2页
摘 要: GA是一类基于自然选择和遗传学原理的有效搜索方法,它从一个种群开始,利用选择、交叉、变异等遗传算子对种群进行不断进化,最后得到全局最优解。但随着求解问题的复杂性及难度的增加,提高GA的运行速度便显得尤为突出,采用并行遗传算法(PGA)是提高搜索效率的方法之一。本文分析了并行遗传算法的四种模型,最后应用于0-1背包问题的求解。实验结果表明,该算法在具有较高搜索效率的同时,仍能维持很高的种群多样性。 Genetic Algorithm (GA) is one self-adaptive universal optimization searching algorithm, formed by attempting to simulate biological process of inheritance and evolution in natural environment. Although GA has a powerful quality of global search, it has low search efficiency in the late evolving period. This paper puts forward a Parallel Genetic Algorithm (PGA) and is applied to solve knapsack problem. Experimental result shows that PGA has good ability of global optimization, and good ability of diversity reservation.
领 域: [自动化与计算机技术] [自动化与计算机技术]