A Quantum-inspired Cooperative Co-evolution Based Minimum Attribute Reduction Algorithm and Its Application in Medical MRI

摘要

In this paper, a novel quantum-inspired cooperative co-evolution based minimum attribute reduction algorithm (QCCE-MAR) incorporated into the shuffled frog leaping algorithm is proposed. This algorithm determines to accelerate the convergence speed with multi-state Q-bit representation, and a cooperative co-evolutionary model for attribute reduction among mutual evolution grouped frog memeplexes helps to increase the diversity and avoid prematurity. Each sub-optimal reduction set can be obtained quickly. The QCCE-MAR algorithm is applied into the medical Magnetic Resonance Imaging (MRI) reduction and segmentation in the incomplete electronic patient record system. Some experiments conducted demonstrate this algorithm is better on both quality of solution and computational complexity for minimum attribute reduction than traditional algorithms, and it is more efficient performance for medical MRI reduction and segmentation.