Knowledge reduction algorithm for rough sets based on adaptive genetic algorithm

In order to achieve effectively at tribute reduction, the paper proposes a rough set attribute reduction algorithm based on AGA. The core is joined initial population in AGA in order to accelerate capability. According to the dependability of decision attribute to the condition attribute, it can but only obtain the capability of part searching, but also retain the peculiarity of all searching. The adaptive crossover probability and adaptive mutation probability are designed, considering the influence of every generation to algorithm and the effect of different individual fitness in every generation. Experimental results show that the accurate reduction and the average algebraic sum all obtain the preferable values.