Modified Heuristic Genetic Algorithm For Reduction Of Attributes

Rough set theory, proposed by Pawlak in the early 1980s,is a mathematical theory to deal with vague and uncertain knowledge,which can analyze the rule hidden in the data without any additional information.Rough set theory is used in many fields such as knowledge discovery, pattern identifying, decision analysis and machine learning.The attribute reduction of information system is one of the main topics in rough set theory. Getting the best reduction or all reduction is the NP-hard problem. Heuristic algorithm based on the attribute importance had been made to get better reduction quickly.In this paper, we regard the significance of attributes defined from the approximation quality with respect to the partition and condition attributes set as heuristic information, and introduce the heuristic information into genetic algorithm based on optimizing initial population. A new modify operator is used for introducing the heuristic information so as to maintain the ability of classification of the attributes set. The operator is an embodiment of local research method using heuristic information. So the algorithm converges quickly and has global optimizing ability. The algorithm is also analyzed theoretically and the correctness and effectiveness of the algorithm are shown in the experiments.