基于动态交叉协同的属性量子进化约简与分类学习级联算法

属性约简与规则分类学习是粗糙集理论研究和应用的重要内容。文中充分利用量子计算加速算法速度和混合蛙跳算法高效协同搜索等优势，提出一种基于动态交叉协同的量子蛙跳属性约简与分类学习的级联算法。该算法用量子态比特进行蛙群个体编码，以动态量子角旋转调整策略实现属性染色体快速约简，并在粗糙熵阈值分类标准内采用量子蛙群混合交叉协同进化机制提取和约简分类规则、组合决策规则链等，最后构造属性约简和分类学习双重功能级联模型。仿真实验验证该算法不仅具有较高的全局优化性能，且属性约简与规则分类学习的精度和效率均超过同类算法。

A Cascade Algorithm of Quantum Attribute Evolution Reduction and Classification Learning Based on Dynamic Crossover Cooperation

Attribute reduction and rule classification learning are important contents for research and application of rough set theory. Taking advantage of quantum computing to accelerate the algorithm speed and co-searching of shuffled frog leaping algorithm, a cascade algorithm of attribute reduction and classification learning based on the dynamic quantum frog-leaping crossover cooperation is proposed. Individuals in the frog swarm are represented by multi-state gene qubits, and the dynamic adjustment strategy of quantum rotation angle is applied to accelerate its convergence. By the crossover coevolution mechanism, classification rules are extracted and reduced, and decision rule chains are introduced in the classification criterion of rough entropy thresholding. The double cascade model of attribute reduction and classification learning is constructed. Experimental simulations indicate the proposed algorithm has good performance for global optimization. Compared with other algorithms, it is more efficient on attribute reduction and rule classification learning.