Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
This study presents a dynamic Quantum-Inspired Genetic Algorithm (D-QIGA) for
feature selection, leveraging quantum principles like superposition and
rotation gates to enhance exploration and exploitation. D-QIGA introduces
adaptive mechanisms and a lengthening chromosome strategy to avoid local optima
and improve optimization. Tested on benchmark and real-world problems, it
significantly outperforms traditional Genetic Algorithms, achieving over 99.99%
classification accuracy compared to GA's 95%.
