A modified heuristic genetic algorithm for reduction of attributes in rough set theory

Attribute reduction is one of the key problems in knowledge discovery. A modified heuristic genetic algorithm based on optimizing initial population is proposed to effectively achieve the minimal relative reduction of the attributes in a decision table. The effect of attribute subclass on certain classification subset in universe is described by constructing a new operator and regarding the significance of the attributes defined from the viewpoint of information theory as heuristic information. Then, the optimized chromosomes are selected as initial population in order to enhance the ability of local search of the algorithm and to maintain the feature of global search of it. Finally, the algorithm is analyzed in theory and it is proven that its local search ability is enhanced and its global search specialty is maintained simultaneously. The experimental results show that this algorithm is effective to the attribute reduction of decision tables.