A novel quantum cooperative co-evolutionary algorithm for large-scale minimum attribute reduction optimization

Due to the fact that conventional evolution-based attribute reduction algorithms are poor efficiency in accomplishing large-scale attribute reduction, a novel and efficient quantum cooperative co-evolutionary algorithm (named QCCAR) for minimum attribute reduction optimization in large-scale datasets is proposed in this paper. First, the self-adaptive quantum rotation angle and quantum entanglement strategies are adopted to update the operation of quantum revolving door, and the population diversity and convergence to the global optimum ensure to be improved fast. Second, a local-global best performance based cooperative co-evolutionary paradigm is designed to divide large-scale attribute sets into reasonable subsets, which are adaptively produced based on the assignment of decomposer credit and probability. Third, the representative of the subpopulation is selected to evolve the corresponding decomposed attribute subset so that the global optimization reduction set can be obtained quickly. The experimental results demonstrate that the proposed algorithm has better feasibility and effectiveness, comparison with other state-of-the-art algorithms. So it can provide an efficient solution to finding minimum attribute reduction for large-scale datasets.