Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915228634054656 |
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| author | Singh, Akhilesh Kumar Hiremath, Kirankumar R. |
| author_facet | Singh, Akhilesh Kumar Hiremath, Kirankumar R. |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm Singh, Akhilesh Kumar Hiremath, Kirankumar R. Neural and Evolutionary Computing 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%. |
| title | Image Classification Method using Dynamic Quantum Inspired Genetic Algorithm |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2501.11477 |