基于Pawlak算法的物流选址指标建模研究

文章从物流节点选址指标体系的复杂性入手,提出以Pawlak算法对指标体系进行约简,简化了物流选址的复杂性。并通过实例展示该算法在物流选址中的应用,结果表明该算法是可行和有效的,并具有很好的实际应用价值。它为物流节点选址问题的解决提供了新的可行途径和有效手段。

Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.

基于Rough理论属性重要性的遗传计算方法

遗传算法提供求解复杂系统优化问题的通用框架,Rough集理论的属性依赖度可以确定各属性对系统分类的重要性。本文通过一个信息表实例将遗传算法和Rough集理论结合起来以计算属性的重要性,两者结合能有效进行属性重要性的计算,并能进行计算机自动计算和信息处理。