Computational modeling of multi-objective pedal parameter optimization in polyphonic piano systems using genetic algorithms

Genetic algorithms (GAs) provide a robust approach to solving multi-objective optimization problems in computational music, particularly in the intricate context of polyphonic piano performance. This study introduces a GA-based framework to optimize pedal usage by balancing tonal clarity, harmonic richness, and synchronization with musical phrasing. To ensure accurate and practical modeling, the framework integrates acoustic simulations and data collected by optical sensors such as high-speed cameras and laser vibrometers, which capture precise pedal movements and vibration characteristics of the piano strings. These advanced data collection methods significantly improve the fidelity of resonance and decay models. Through adaptive fitness evaluation and iterative evolution via selection, crossover, and mutation, the GA explores vast solution spaces effectively. Experimental evaluations demonstrate a 23% improvement in tonal clarity, enhanced harmonic richness, and a synchronization score of 0.92, surpassing traditional heuristic methods. Case studies highlight reduced harmonic overlap and improved temporal alignment, validating the framework's adaptability across diverse musical passages. Sensitivity analysis reveals the influence of mutation rates and crossover probabilities on optimization outcomes, emphasizing the importance of parameter tuning. This computational framework bridges the gap between algorithmic modeling and musical expression, offering a systematic and reproducible method for enhancing performance quality. By advancing the understanding and application of pedal control in polyphonic systems, this research provides a significant step forward in computational music technology, enabling precise and expressive piano performance.