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Editorial: Metaheuristic Search in Medical Data Analysis

| Caglar Cengizler |

Year: 2023 | Vol: 2 | No: 3 | PP 17-20
Abstract
Metaheuristic algorithms are designed to explore the provided search space, try to discover the optimum solution. These algorithms are tailored tools for searching and exploiting all possible alternatives. Most of the metaheuristic algorithms are inspired from nature to mimic the natures ability to invent solution for sophisticated problems. One area that benefits from the search capabilities of metaheuristic algorithms is medical data analysis.

Keywords
Editorial; Metaheuristic; Medical; Data
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References
  1. Zamani, H. and Nadimi-Shahraki, M-H. Feature selection based on whale optimization algorithm for diseases diagnosis. In International Journal of Computer Science and Information Security, 14 (9): 1243, 2016.
  2. Nadimi-Shahraki, M. H; Zamani, H. and Mirjalili, S. Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. In Computers in biology and medicine, 148: 105858, 2022.
  3. Binu, D and Selvi, M BFC: Bat algorithm based fuzzy classifier for medical data classification. In Journal of Medical Imaging and Health Informatics, 5 (3): 599-606, 2015.
  4. Jeyasingh, S. and Veluchamy, M. Modified bat algorithm for feature selection with the wisconsin diagnosis breast cancer (WDBC) dataset. In Asian Pacific journal of cancer prevention: APJCP, 18 (5): 1257, 2017.
  5. Sharma, P. and Sharma, K. Fetal state health monitoring using novel Enhanced Binary Bat Algorithm. In Computers and Electrical Engineering, 101: 108035, 2022.
  6. Chakraborty, C.; Kishor, A. and Rodrigues, J. J. Novel Enhanced-Grey Wolf Optimization hybrid machine learning technique for biomedical data computation. In Computers and Electrical Engineering, 99: 107778, 2022.
  7. Yang, X-S. and He, X. Firefly algorithm: recent advances and applications. In International journal of swarm intelligence, 1 (1): 36-50, 2013.
  8. Sahmadi, B.; Boughaci, D.; Rahmani, R. and Sissani, N. A modified firefly algorithm with support vector machine for medical data classification. In Computational Intelligence and Its Applications: 6th IFIP TC 5 International Conference, CIIA 2018, Oran, Algeria, May 8-10, 2018, Proceedings 6, pages 232-243, 2018.
  9. Glover, F.; Laguna, M. and Marti, R. Principles of tabu search. In Approximation algorithms and metaheuristics, 23: 1-12, 2007.
  10. Demeester, P.; Souffriau, W.; De Causmaecker, P. and Berghe, G. V. A hybrid tabu search algorithm for automatically assigning patients to beds. In Artificial Intelligence in Medicine, 48 (1): 61-70, 2010.
  11. Blum, C. Ant colony optimization: Introduction and recent trends. In Physics of Life reviews, 2 (4): 353-373, 2005.
  12. Asha, A A.; Victor, SP and Lourdusamy, A Feature extraction in medical image using ant colony optimization: a study. In International Journal on Computer Science and Engineering, 3 (2): 714-721, 2011.