[
[1] T. Güneş, ‘Parkinson hastalığı nedir? Belirtileri nelerdir?’ https://www.medicalpark.com.tr/parkinson-hastaligi/hg-1976 (accessed Jan. 12, 2022).
]Search in Google Scholar
[
[2] C. Marras et al., ‘Prevalence of Parkinson’s disease across North America’, NPJ Parkinson’s disease, vol. 4, no. 1, pp. 1–7, 2018.10.1038/s41531-018-0058-0603950530003140
]Search in Google Scholar
[
[3] D. Kaya, ‘Optimization of SVM parameters with hybrid CS-PSO algorithms for Parkinson’s disease in LabVIEW environment’, Parkinson’s Disease, vol. 2019, 2019.10.1155/2019/2513053652579931191900
]Search in Google Scholar
[
[4] A. Narı̇ n, ‘Effect of features selected by ant colony algorithm in performance of parkinson’s patients’, Duzce University Journal of Science and Technology, vol. 8, no. 4, pp. 2443–2454, Oct. 2020, doi: 10.29130/dubited.650958.10.29130/dubited.650958
]Search in Google Scholar
[
[5] S. Ayhan and Ş. Erdoğmuş, ‘Kernel function selection for the solution of classification problems via support vector machines’, Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, vol. 9, no. 1, pp. 175–198, 2014.
]Search in Google Scholar
[
[6] S. Osowski, K. Siwek, and T. Markiewicz, ‘MLP and SVM Networks-a Comparative Study’, in Proceedings of the 6th Nordic Signal Processing Symposium, 2004. NORSIG 2004., 2004, pp. 37–40.
]Search in Google Scholar
[
[7] I. Nitze, U. Schulthess, and H. Asche, ‘Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification’, Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, vol. 79, p. 3540, 2012.
]Search in Google Scholar
[
[8] X.-S. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons, 2010.10.1002/9780470640425
]Search in Google Scholar
[
[9] H. Han and W. Houjun, ‘Study on power transformer protection based on chaos particle swarm optimization’, in IEEE 2011 10th International Conference on Electronic Measurement & Instruments, 2011, vol. 4, pp. 247–250.10.1109/ICEMI.2011.6037988
]Search in Google Scholar
[
[10] J. Wang, G. Shan, Q. Zhang, and X. Duan, ‘Analog circuit Fault Diagnosis Method Based on CPSO-SVM’, in 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 2011, pp. 465–468.10.1109/ICQR2MSE.2011.5976654
]Search in Google Scholar
[
[11] O. Ceylan and G. Taşkın, ‘SVM parameter selection based on harmony search with an application to hyperspectral image classification’, in 2016 24th Signal Processing and Communication Application Conference (SIU), 2016, pp. 657–660.10.1109/SIU.2016.7495825
]Search in Google Scholar
[
[12] O. Ceylan and G. Taşkın, ‘A Comparison of differential evolution and harmony search methods for SVM model selection in hyperspectral image classification’, in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 485–488.10.1109/IGARSS.2016.7729120
]Search in Google Scholar
[
[13] I. Aljarah, A. M. Al-Zoubi, H. Faris, M. A. Hassonah, S. Mirjalili, and H. Saadeh, ‘Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm’, Cogn Comput, vol. 10, no. 3, pp. 478–495, Jun. 2018, doi: 10.1007/s12559-017-9542-9.10.1007/s12559-017-9542-9
]Search in Google Scholar
[
[14] X. Wu, ‘Research on the Influence of Crossover Probability and Mutation Probability in GA-SVM Model’, in 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), 2019, pp. 1–7.10.1109/ICEIEC.2019.8784498
]Search in Google Scholar
[
[15] L. Jun, D. Lixin, and X. Ying, ‘Differential evolution based parameters selection for support vector machine’, in 2013 Ninth International Conference on Computational Intelligence and Security, 2013, pp. 284–288.10.1109/CIS.2013.67
]Search in Google Scholar
[
[16] L. Shen et al., ‘Evolving Support Vector Machines using Fruit Fly Optimization for Medical Data Classification’, Knowledge-Based Systems, vol. 96, pp. 61–75, 2016.10.1016/j.knosys.2016.01.002
]Search in Google Scholar
[
[17] B. Tezcan, ‘Determination of Support Vector Machine Parameters By Meta Heuristic Optimization Algorithms’, Master’s Thesis, Selçuk University, Turkey, 2018.
]Search in Google Scholar
[
[18] X.-S. Yang, ‘A New Metaheuristic Bat-Inspired Algorithm’, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, 2010, pp. 65–74.10.1007/978-3-642-12538-6_6
]Search in Google Scholar
[
[19] B. Liu, L. Wang, Y.-H. Jin, F. Tang, and D.-X. Huang, ‘Improved particle swarm optimization combined with chaos’, Chaos, Solitons & Fractals, vol. 25, no. 5, pp. 1261–1271, 2005.
]Search in Google Scholar
[
[20] Z. W. Geem, J. H. Kim, and G. V. Loganathan, ‘A new heuristic optimization algorithm: harmony search’, simulation, vol. 76, no. 2, pp. 60–68, 2001.10.1177/003754970107600201
]Search in Google Scholar
[
[21] M. Little, P. McSharry, S. Roberts, D. Costello, and I. Moroz, ‘Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection’, Nature Precedings, pp. 1–1, 2007.10.1038/npre.2007.326.1
]Search in Google Scholar
[
[22] L. Naranjo, C. J. Perez, Y. Campos-Roca, and J. Martin, ‘Addressing voice recording replications for Parkinson’s disease detection’, Expert Systems with Applications, vol. 46, pp. 286–292, 2016.10.1016/j.eswa.2015.10.034
]Search in Google Scholar
[
[23] L. Naranjo, C. J. Perez, J. Martin, and Y. Campos-Roca, ‘A two-stage variable selection and classification approach for Parkinson’s disease detection by using voice recording replications’, Computer methods and programs in biomedicine, vol. 142, pp. 147–156, 2017.10.1016/j.cmpb.2017.02.01928325442
]Search in Google Scholar
[
[24] V. Vapnik, The nature of Statistical Learning Theory. Springer Science & Business Media, 1999.10.1007/978-1-4757-3264-1
]Search in Google Scholar
[
[25] T. Kavzoğlu and İ. Çölkesen, ‘Investigation of the effects of kernel functions in satellite image classification using support vector machines’, Gebze High Technology Institute The Journal of Map, vol. 144, pp. 73–82, 2010.
]Search in Google Scholar
[
[26] L. Wang, Support Vector Machines: Theory and Applications, vol. 177. Springer Science & Business Media, 2005.10.1007/b95439
]Search in Google Scholar
[
[27] R. M. Brigham, E. K. V. Kalko, G. Jones, S. Parsons, and H. Limpens, ‘Bat echolocation research: tools, techniques and analysis’, Bat Conservation International. Austin, Texas, 2004.
]Search in Google Scholar
[
[28] L. L. Kamal, ‘Training Artificial Neural Networks Using Bat Optimization Algorithm’, Master’s Thesis, Selçuk University, Turkey, 2018.
]Search in Google Scholar
[
[29] A. Tharwat, A. E. Hassanien, and B. E. Elnaghi, ‘A BA-Based Algorithm for Parameter Optimization of Support Vector Machine’, Pattern Recognition Letters, vol. 93, pp. 13–22, 2017.10.1016/j.patrec.2016.10.007
]Search in Google Scholar
[
[30] X.-S. Yang, Nature-inspired optimization algorithms. Academic Press, 2020.10.1016/B978-0-12-821986-7.00018-4
]Search in Google Scholar
[
[31] X.-S. Yang and X. He, ‘Bat Algorithm: Literature Review and Applications’, International Journal of Bio-inspired Computation, vol. 5, no. 3, pp. 141–149, 2013.10.1504/IJBIC.2013.055093
]Search in Google Scholar
[
[32] J. Kennedy and R. Eberhart, ‘Particle swarm optimization’, in Proceedings of ICNN’95-international conference on neural networks, 1995, vol. 4, pp. 1942– 1948.
]Search in Google Scholar
[
[33] X. Gao, P. Yu, W. Mao, and D. Peng, ‘Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization’, in 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, 2012, pp. 1–4.10.1109/RSETE.2012.6260436
]Search in Google Scholar
[
[34] Y. Cheng, Y. Fu, and G. Gong, ‘A Hybrid Support Vector Regression Based on Chaotic Particle Swarm Optimization Algorithm in Forecasting Financial Returns’, in 2010 2nd International Conference on Information Engineering and Computer Science, Dec. 2010, pp. 1–4. doi: 10.1109/ICIECS.2010.5678364.10.1109/ICIECS.2010.5678364
]Search in Google Scholar
[
[35] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.10.7551/mitpress/1090.001.0001
]Search in Google Scholar
[
[36] H. Dawid, Adaptive learning by genetic algorithms: Analytical results and applications to economic models. Springer Science & Business Media, 2011.
]Search in Google Scholar
[
[37] E. Ayhan, ‘Blood supply chain distribution network modeling and solution with hybrid genetic algorithm’, PhD Thesis, Yıldız Technical University, Turkey, 2021.
]Search in Google Scholar
[
[38] B. Bolat, K. O. EROL, and C. E. İMRAK, ‘Genetic algorithms in engineering applications and the Function of operators’, Sigma, vol. 4, pp. 264–271, 2004.
]Search in Google Scholar
[
[39] E. Okur and M. Atlas, ‘Solution of vehicle routing problem with genetic algorithm’, Anadolu University Journal of Social Sciences, vol. 20, no. 3, Art. no. 3, Sep. 2020, doi: 10.18037/ausbd.801768.10.18037/ausbd.801768
]Search in Google Scholar
[
[40] S. Çolak, ‘Genetik Algoritmalar Yardımı ile Gezgin Satıcı Probleminin Çözümü Üzerine Bir Uygulama’, Journal of the Cukurova University Institute of Social Sciences, vol. 19, no. 3, Art. no. 3, Sep. 2010.
]Search in Google Scholar
[
[41] T.-Y. Chen and C.-J. CHEN, ‘Improvements of simple genetic algorithm in structural design’, International Journal for Numerical Methods in Engineering, vol. 40, no. 7, pp. 1323–1334, 1997.
]Search in Google Scholar
[
[42] A. Kalinli and Ö. Aksu, ‘Genetic algorithm model based on dominant gene selection operator’, 2011.
]Search in Google Scholar
[
[43] S. Güngör, ‘Harmony Search Algorithm for Team Formation Problems: An Application in Kayserispor Football Team’, Master’s Thesis, Eriyes University, Turkey, 2015.
]Search in Google Scholar
[
[44] M. T. Ayvaz, H. Karahan, and G. Gurarslan, ‘Su dağıtım şebekelerinin armoni araştırması optimizasyon tekniği ile optimum tasarımı, 5’, 2007.
]Search in Google Scholar
