Genetic Algorithm Performance Enhancement through Adaptive Techniques and Elitism

This paper presents an enhanced Genetic Algorithm (GA), implemented in Python and exposed through Django API endpoints, that integrates adaptive techniques and elitism to improve efficiency and convergence speed. The proposed approach dynamically adjusts mutation and crossover probabilities based on population diversity and stagnation metrics, while an elitism mechanism ensures the preservation of top-performing solutions across generations. Using a permutation-based chromosome structure designed to evolve a target musical note sequence, the adaptive GA achieves significant improvements-reducing the required generations from ۸۳ (with fixed rates) to ۲۱ (with adaptive rates)-thereby minimizing local optima traps and promoting exploration. The system is further integrated with MIDO for MIDI file generation and React for visualization, enabling real-time monitoring of evolutionary progress, error magnitudes, and standard deviations. The method provides both mathematical formulations and implementation process, demonstrating how standard GAs can be transformed into robust optimization tools with applications in sequence evolution and other complex problem domains.

کلیدواژه ها:

Genetic Algorithm ، Adaptive Mutation ، Adaptive Crossover ، Elitism ، Population Diversity ، Convergence Acceleration