A Minimum Attribute Self-Adaptive Cooperative Co-Evolutionary Reduction Algorithm Based on Quantum Elitist Frogs

Attribute reduction is a key point in studying rough sets theory. It has been proven that computing minimum attribute reduction of the decision table is an NP-hard problem. However, the conventional evolutionary algorithms are not efficient in accomplishing minimum attribute reduction. A novel minimum attribute self-adaptive cooperative co-evolutionary reduction algorithm (QEFASCR) based on quantum elitist frogs is proposed. Firstly, evolutionary frogs are represented by multi-state quantum chromosomes, and quantum elitist frogs can fast guide the evolutionary frogs into the optimal area, which can strengthen the convergence velocity and global search efficiency. Secondly, a self-adaptive cooperative co-evolutionary model for minimum attribute reduction is designed to decompose evolutionary attribute sets into reasonable subsets according to both the best historical performance experience records and assignment credits, and some optimal elitists in different subpopulations are selected out to evolve their respective attribute subsets, which can increase the cooperation and efficiency of attribute reduction. Therefore the global minimum attribute reduction set can be obtained quickly. Experiments results indicate that the proposed algorithm can achieve the higher performance on the efficiency and accuracy of minimum attribute reduction, compared with the existing algorithms.