An adaptive minimum attribute reduction algorithm integrating quantum elitists and reverse cloud models

In this paper, an adaptive and efficient minimum attribute reduction algorithm (QERCMAR) integrating quantum elitists and reverse cloud models is proposed. First, the quantum chromosome is used to encode the evolutionary population, and a multilevel elitist pool of quantum frogs is constructed, in which quantum elitist frogs can fast guide the evolutionary population into the optimal area. Second, a reverse cloud mode based on the attribute entropy weight is designed to adjust the quantum revolving gate so that the scope of a search space can be adaptively controlled under the guidance of qualitative knowledge. In addition, both the quantum reverse cloud mutation and quantum reverse cloud entanglement operators are used to make quantum frogs be adaptive to attain the minimum attribute reduction set much faster. Experimental results indicate the QERCMAR algorithm can achieve the superior performance. The effective and robust segmentation results in the Bladder MRI further demonstrate it has stronger applicability.