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• A Genetic Programming (GP) pipeline can automatically evolve composite features by mathematically combining base music features for music tagging.method 95%
• The proposed GP approach provides representational benefits similar to deep feature fusion while preserving interpretability.technical 90%
• The method demonstrates consistent performance improvements over state-of-the-art systems on the MTG-Jamendo and GTZAN datasets.result 85%
• Most performance gains are achieved within the first few hundred GP evaluations, indicating effective feature combinations can be found with a modest search budget.result 80%
• The top evolved expressions include linear, nonlinear, and conditional forms, with low-complexity solutions achieving top performance due to parsimony pressure.result 85%
• Analyzing the evolved composite features reveals which feature interactions and transformations are beneficial for tagging, offering insights not available in black-box deep models.technical 80%
• The GP implementation uses specific parameters: population sizes of 100 and 500, termination after 50 generations or early stopping based on stagnation or convergence criteria.method 90%

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