A Novel Minimum Attribute Reduction Algorithm Based on Hierarchical Elitist Role Model Combining Competitive and Cooperative Co-evolution

Minimum attribute reduction in rough set theory is an NP-hard problem, which is difficult to use traditional evolution methods to solve. In this paper, a novel and efficient minimum Attribute reduction algorithm (named HERCo²AR) based on Hierarchical elitist role model combining competitive and cooperative coevolution is proposed. Through such an iterative process of competitive and cooperative coevolution, the various subpopulations are better optimized by different elitists, and reasonable decomposition of interacting attribute sets can coadapt to emerge due to the evolutionary pressure of hierarchical elitist role. The hierarchical elitist role model is very effective in the protection and promotion of outstanding individuals, and it can accelerate to direct the global optimal attribute reduction. Experimental results demonstrate that HERCo²AR achieves the better feasibility and effectiveness than existing state-of-the-art attribute reduction algorithms, and the quality of the global optimal solution can be signi-cantly improved as well.