# Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm

Source: [Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm](<https://www.sota2.com/research/paper/68fa77e01e209131cea75c17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:28:27.917Z

Authors: Akhilesh Kumar Singh, Kirankumar R. Hiremath

Published: 2025-01-20T13:18:13.000Z

## 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%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.11477>)

[PDF](<https://arxiv.org/pdf/2501.11477.pdf>)