A Research For Algorithms Of Attribute Reduction Base On Grnetic Algorithms And Information Entropy

Rough set theory, initialized by Professor Z.Pawlak in early 1980's, has been proved to be an excellent mathematical tool dealing with uncertain and vague description of objects, whose basic idea is to derive classification rules of conception by knowledge reduction with the ability of classification unchanged. It may find the hiding and potential rules, that is knowledge, from the data without any preliminary or additional information. In recent years, as an important part of soft computing, rough set theory and its applications have played an important role, especially in the areas of pattern recognition, machine learning, decision analysis, knowledge discovery and knowledge acquisition etc.Firstly,this classical Pawlak rough ests based on the equivalence relation is introduced and genetic algorithms information entropy.Secondly.It introduce basic ideology for genetic algorithm and information theory.Then posed the news algorithm of attribute reduction.Regarding the significance of attributes defined from the viewpoint of information theory as heuristic information.An effective heuristic genetic algorithm for minimizing relative reduction is proposed. A new operator is used for introducing the heuristic information so as to maintain the ablity of acassification of the attributes set.It main using to seek the relative of attribute reduction.