# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 3 Knowledge-Guided Evolutionary Algorithm
### Feature Grouping Based on Correlation
The advantage is particularly notable in datasets with complex feature interactions, such as ISOLET5 and Toxic, where the knowledge-guided approach of the KCCEA significantly improves solution quality by effectively navigating the expansive search space. This performance enhancement stems from the structured crossover and mutation processes of the KCCEA, which utilize feature correlations to maintain relevant interactions and reduce computational overhead, enabling faster convergence towards optimal solutions. Additionally, the KCCEA framework adjusts allocation ratios dynamically based on real-time performance metrics, which helps the algorithm adapt to the most promising solution paths and prevents premature convergence to suboptimal solutions. This strategic integration of knowledge within evolutionary cycles substantiates the effectiveness of the KCCEA approach, marking it as a substantial improvement over traditional methods in tackling the complexities of advanced feature selection. ### Discussion

The experimental results demonstrate the efficacy of the proposed KCCEA for high-dimensional feature selection. The KCCEA's superior performance, indicated by lower IGD values and higher HV values across various datasets, underscores the advantages of integrating domain knowledge and competitive-cooperative mechanisms into evolutionary algorithms. Furthermore, the design of the KCCEA considers the scalability to handle large-scale datasets. Our proposed feature correlation knowledge grouping only requires a one-time calculation using the given algorithm before the evolution starts to acquire the knowledge. This grouping method demonstrates excellent efficiency and scalability, making it easily applicable to large-scale datasets. While the KCCEA shows significant improvements in search efficiency and solution quality, there are still some limitations in this work. For instance, noisy and imbalanced data can significantly impact the effectiveness of feature selection [41]. It would be valuable to analyze the robustness of the algorithm under different conditions, such as in the presence of noise or data imbalance. Additionally, exploring the applicability of the KCCEA to other domains, such as regression and unsupervised learning tasks, could be interesting future work. Considering the scope of this work, data complexity and imbalance will be addressed in future studies. Furthermore, traditional machine learning methods often require prior feature selection, whereas neural networks [42] might perform better with selected features. Therefore, it is valuable to evaluate the proposed method's performance before and after feature selection using neural networks. Exploring the performance of various classifiers with the KCCEA could also be considered in future work. Despite these challenges, the KCCEA represents a significant advancement in the field of high-dimensional feature selection. ## 7 Conclusions

This article proposes and examines the Knowledge-Guided Competitive-Cooperative Evolutionary Algorithm (KCCEA), demonstrating its significant advancements in the field of complex feature selection within high-dimensional datasets. Our paper not only provides an analysis but also proposes the integration of the Knowledge-Guided Evolutionary Algorithm (KGEA) with the traditional NSGA-II under a competitive-cooperative framework, which enhances the evolutionary strategy and efficiently navigates the expanded search space. The theoretical contribution of this research is the novel method of embedding domain-specific knowledge within the evolutionary processes, optimizing genetic operations and significantly boosting the algorithm's performance. On a practical level, the KCCEA's dynamic resource allocation strategy, which adjusts based on real-time performance metrics, ensures the algorithm continuously adapts to the most promising solution paths, thus preventing premature convergence to suboptimal solutions.