Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm

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%.

Tasks

Reproductions