# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 3 Knowledge-Guided Evolutionary Algorithm
### Feature Grouping Based on Correlation
This adaptability underscores the practical utility of the KCCEA in real-world applications where decision variables are complexly interrelated. Nevertheless, the approach is heavily reliant on the initial accuracy of the domain knowledge used to guide the feature grouping, which is a limitation. Future work could address this by exploring autonomous methods for knowledge acquisition and integration. Further research might also consider applying the competitive-cooperative framework to other optimization domains, potentially broadening the scope and impact of this advanced evolutionary approach. Conceptualization, J.Z. and H.Z. (Haowen Zheng); Methodology, J.Z. ; Software, J.Z. ; Validation, J.Z., H.Z. (Haoyang Zhang), and X.W. ; Formal Analysis, J.Z. ; Investigation, S.L. and Q.H. ; Resources, W.G. ; Data Curation, H.Z. ([PERSON]); Writing--Original Draft Preparation, J.Z. ; Writing--Review and Editing, X.W. ; Visualization, J.Z. ; Supervision, X.W. ; Project Administration, X.W. ; Funding Acquisition, W.G. and X.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Training Program of Innovation and Entrepreneurship for Undergraduates, Project 241221. ## References

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