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Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm

Year: 2025

Type: preprint

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%.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2501.11477

Authors: Singh, Akhilesh Kumar, Hiremath, Kirankumar R.

Institution: Indian Institute of Technology Jodhpur

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4406741135

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