A dynamic multitask evolutionary algorithm for high-dimensional feature selection based on multi-indicator task construction and elite competition learning.

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, which integrates competitive learning and knowledge transfer within an evolutionary optimization setting. The framework begins by generating two complementary tasks through a multi-criteria strategy that combines multiple feature relevance indicators, ensuring both global comprehensiveness and local focus. These tasks are optimized in parallel using a competitive particle swarm optimization algorithm enhanced with hierarchical elite learning, where each particle learns from both winners and elite individuals to avoid premature convergence. To further improve optimization efficiency and diversity, a probabilistic elite-based knowledge transfer mechanism is introduced, allowing particles to selectively learn from elite solutions across tasks. Experimental results on 13 high-dimensional benchmark datasets demonstrate that the proposed algorithm achieves superior classification accuracy with fewer selected features compared to several state-of-the-art methods. Across 13 benchmarks, the proposed method achieves the highest accuracy on 11 out of 13 datasets and the fewest features on eight out of 13, with an average accuracy of 87.24% and an average dimensionality reduction of 96.2% (median 200 selected features), clearly validating its effectiveness in balancing exploration, exploitation, and knowledge sharing for robust feature selection.