Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

 References

• Ahmad, I., Hussain, M., Alghamdi, A., & Alelaiwi, A. (2014). Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components. Neural Computing & Applications, 24(7–8), 1671–1682. https://doi.org/10.1007/s00521-013-1370-6
   Web of Science ®Google Scholar
• Alamiedy, T. A., Anbar, M., Alqattan, Z. N., & Alzubi, Q. M. (2019). Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. Journal of Ambient Intelligence and Humanized Computing, (2019 Nov 9), 1-22. https://doi.org/10.1007/s12652-019-01569-8
   Google Scholar
• Aličković, E., & Subasi, A. (2017). Breast cancer diagnosis using GA feature selection and rotation forest. Neural Computing & Applications, 28(4), 753–763. https://doi.org/10.1007/s00521-015-2103-9
   Web of Science ®Google Scholar
• Alzubi, Q. M., Anbar, M., Alqattan, Z. N., Al-Betar, M. A., & Abdullah, R. (2019). Intrusion detection system based on a modified binary grey wolf optimisation. Neural Computing & Applications, 32, 6125–6137. https://doi.org/10.1007/s00521-019-04103-1
   Google Scholar
• Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2015). Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(5), 971–989. https://doi.org/10.1109/TCBB.2015.2478454
   PubMed Web of Science ®Google Scholar
• Bache, K., & Lichman, M. (2013). UCI machine learning repository. https://archive.ics.uci.edu/ml/index.php
   Google Scholar
• Balasaraswathi, V. R., Sugumaran, M., & Hamid, Y. (2017). Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. Journal of Communications and Information Networks, 2(4), 107–119. https://doi.org/10.1007/s41650-017-0033-7
   Google Scholar
• Bamakan, S. M. H., Wang, H., Yingjie, T., & Shi, Y. (2016). An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing, 199(26 July 2016), 90–102. https://doi.org/10.1016/j.neucom.2016.03.031
   Web of Science ®Google Scholar
• Beheshti, Z., & Shamsuddin, S. M. H. (2013). A review of population-based meta-heuristic algorithms. Int. J. Adv. Soft Comput. Appl, 5(1), 1–35. https://www.researchgate.net/publication/270750820
   Google Scholar
• Bostani, H., & Sheikhan, M. (2017). Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft Computing, 21(9), 2307–2324. https://doi.org/10.1007/s00500-015-1942-8
   Web of Science ®Google Scholar
• Bouraoui, A., Jamoussi, S., & BenAyed, Y. (2018). A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines. Artificial Intelligence Review, 50(2), 261–281. https://doi.org/10.1007/s10462-017-9543-9
   Web of Science ®Google Scholar
• Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
   Web of Science ®Google Scholar
• Catania, C. A., Bromberg, F., & Garino, C. G. (2012). An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Systems with Applications, 39(2), 1822–1829. https://doi.org/10.1016/j.eswa.2011.08.068
   Web of Science ®Google Scholar
• Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024
   Web of Science ®Google Scholar
• Chen, Z., Lin, T., Tang, N., & Xia, X. (2016). A parallel genetic algorithm based feature selection and parameter optimization for support vector machine. Scientific Programming, 2016, 10. https://doi.org/10.1155/2016/2739621
   Google Scholar
• Črepinšek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 1–33. https://doi.org/10.1145/2480741.2480752
   Web of Science ®Google Scholar
• Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(3), 131–156. https://doi.org/10.3233/IDA-1997-1302
   Google Scholar
• Dastanpour, A., & Mahmood, R. A. R. (2013). Feature selection based on genetic algorithm and supportVector machine for intrusion detection system. In The second international conference on informatics engineering & information science (ICIEIS2013) (pp. 169–181), Hindawi Publishing Corporation. https://doi.org/10.1155/2016/2739621
   Google Scholar
• Davahli, A., Shamsi, M., & Abaei, G. (2020). Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5581–5609. https://doi.org/10.1007/s12652-020-01919-x
   Web of Science ®Google Scholar
• Davis, L. (1991). Handbook of genetic algorithms. http://papers.cumincad.org/cgi-bin/works/BrowseTreefield=seriesorder=AZ/Show?eaca
   Google Scholar
• De La Iglesia, B. (2013). Evolutionary computation for feature selection in classification problems. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 3(6), 381–407. https://doi.org/10.1002/widm.1106
   Web of Science ®Google Scholar
• De Stefano, C., Fontanella, F., Marrocco, C., & Di Freca, A. S. (2014). A GA-based feature selection approach with an application to handwritten character recognition. Pattern Recognition Letters, 35(0167-8655), 130–141. https://doi.org/10.1016/j.patrec.2013.01.026
   Web of Science ®Google Scholar
• Desale, K. S., & Ade, R. (2015). Genetic algorithm based feature selection approach for effective intrusion detection system. International Conference on Computer Communication and Informatics (ICCCI -2015), Jan. 08 – 10, 2015, Coimbatore, India. ieeexplore.ieee.org
   Google Scholar
• Eesa, A. S., Orman, Z., & Brifcani, A. M. A. (2015). A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Systems with Applications, 42(5), 2670–2679. https://doi.org/10.1016/j.eswa.2014.11.009
   Web of Science ®Google Scholar
• Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19–31. https://doi.org/10.1016/j.swevo.2011.02.001
   Web of Science ®Google Scholar
• Emary, E., Yamany, W., Hassanien, A. E., & Snasel, V. (2015a). Multi-objective gray-wolf optimization for attribute reduction. Procedia Computer Science, 65(2015), 623–632. https://doi.org/10.1016/j.procs.2015.09.006
   Google Scholar
• Emary, E., Zawbaa, H. M., Grosan, C., & Hassenian, A. E. (2015b). Feature subset selection approach by gray-wolf optimization. In Afro-European conference for industrial advancement (pp. 1–13). Cham: Springer.
   Google Scholar
• Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016a). Binary ant lion approaches for feature selection. Neurocomputing, 213, 54–65. https://doi.org/10.1016/j.neucom.2016.03.101
   Web of Science ®Google Scholar
• Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016b). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172(4), 371–381. https://www.sciencedirect.com/science/article/abs/pii/S0925231215010504
   Web of Science ®Google Scholar
• Fan, W., Fox, E. A., Pathak, P., & Wu, H. (2004). The effects of fitness functions on genetic programming‐based ranking discovery for web search. Journal of the American Society for Information Science and Technology, 55(7), 628–636. https://doi.org/10.1002/asi.20009
   Web of Science ®Google Scholar
• Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey wolf optimizer: A review of recent variants and applications. Neural Computing & Applications, 30(2), 413–435. https://doi.org/10.1007/s00521-017-3272-5
   Web of Science ®Google Scholar
• Ferriyan, A., Thamrin, A. H., Takeda, K., & Murai, J. (2017). Feature selection using genetic algorithm to improve classification in network intrusion detection system. In 2017 international electronics symposium on knowledge creation and intelligent computing (IES-KCIC) (pp. 46–49). IEEE, Surabaya, Indonesia,  26-27 Sept. 2017. https://ieeexplore.ieee.org/xpl/conhome/8170163/proceeding
   Google Scholar
• Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2009). Weka-a machine learning workbench for data mining. In Data mining and knowledge discovery handbook (pp. 1269–1277). Springer. https://link.springer.com/chapter/10.1007/978-0-387-09823-4_66
   Google Scholar
• Gallagher, K., & Sambridge, M. (1994). Genetic algorithms: A powerful tool for large-scale nonlinear optimization problems. Computers & Geosciences, 20(7–8), 1229–1236. https://doi.org/10.1016/0098-3004(94)90072-8
   Web of Science ®Google Scholar
• García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Information Sciences, 180(10), 2044–2064. https://doi.org/10.1016/j.ins.2009.12.010
   Web of Science ®Google Scholar
• Ghareb, A. S., Bakar, A. A., & Hamdan, A. R. (2016). Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Systems with Applications, 49(C), 31–47. https://doi.org/10.1016/j.eswa.2015.12.004
   Web of Science ®Google Scholar
• Gheyas, I. A., & Smith, L. S. (2010). Feature subset selection in large dimensionality domains. Pattern Recognition, 43(1), 5–13. https://doi.org/10.1016/j.patcog.2009.06.009
   Web of Science ®Google Scholar
• Guyon, I. (2008). Practical feature selection: From correlation to causality. Mining Massive Data Sets for Security: Advances in Data Mining, Search, Social Networks and Text Mining, and Their Applications to Security, 5, 27–43. https://doi.org/10.3233/978-1-58603-898-4-27
   Google Scholar
• Hamed, T., Dara, R., & Kremer, S. C. (2018). Network intrusion detection system based on recursive feature addition and bigram technique. Computers & Security, 73, 137–155. https://doi.org/10.1016/j.cose.2017.10.011
   Web of Science ®Google Scholar
• Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73. https://doi.org/10.1038/scientificamerican0792-66
   PubMed Web of Science ®Google Scholar
• Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31(2), 231–240. https://doi.org/10.1016/j.eswa.2005.09.024
   Web of Science ®Google Scholar
• Jeong, Y. S., Shin, K. S., & Jeong, M. K. (2015). An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems. Journal of the Operational Research Society, 66(4), 529–538. https://doi.org/10.1057/jors.2013.72
   Web of Science ®Google Scholar
• Kang, S. H., & Kim, K. J. (2016). A feature selection approach to find optimal feature subsets for the network intrusion detection system. Cluster Computing, 19(1), 325–333. https://doi.org/10.1007/s10586-015-0527–8
   Web of Science ®Google Scholar
• Karthick, P. A., Ghosh, D. M., & Ramakrishnan, S. (2018). Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Computer Methods and Programs in Biomedicine, 154(4), 45–56. https://doi.org/10.1016/j.cmpb.2017.10.024
   Web of Science ®Google Scholar
• Khammassi, C., & Krichen, S. (2017). A GA-LR wrapper approach for feature selection in network intrusion detection. Computers & Security, 70, 255–277. https://doi.org/10.1016/j.cose.2017.06.005
   Web of Science ®Google Scholar
• Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2015). Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys & Tutorials, 18(1), 184–208. https://doi.org/10.1109/COMST.2015.2402161
   Web of Science ®Google Scholar
• Kotsiantis, S. (2011). Feature selection for machine learning classification problems: A recent overview. Artificial Intelligence Review, 42(1), 157–176. https://doi.org/10.1007/s10462-011-9230-1
   Google Scholar
• Kumar, G., & Kumar, K. (2012). An information theoretic approach for feature selection. Security and Communication Networks, 5(2), 178–185. https://doi.org/10.1002/sec.303
   Web of Science ®Google Scholar
• Larijani, H., Ahmad, J., & Mtetwa, N. (2019). A heuristic intrusion detection system for internet-of-things (IoT). In Intelligent computing-proceedings of the computing conference (pp. 86–98). Cham: Springer.
   Google Scholar
• Li, J., Zhao, Z., Li, R., & Zhang, H. (2018). Ai-based two-stage intrusion detection for software defined iot networks. IEEE Internet of Things Journal, 6(2), 2093–2102. https://doi.org/10.1109/JIOT.2018.2883344
   Web of Science ®Google Scholar
• Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., & Tian, X. (2017). An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Computational and Mathematical Methods in Medicine, 2017, 1748-670X. https://doi.org/10.1155/2017/9512741
   Google Scholar
• Lin, K. C., Huang, Y. H., Hung, J. C., & Lin, Y. T. (2015). Feature selection and parameter optimization of support vector machines based on modified cat swarm optimization. International Journal of Distributed Sensor Networks, 11(7), 365869. https://doi.org/10.1155/2015/365869
   Google Scholar
• Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441–453. https://doi.org/10.1016/j.asoc.2017.11.006
   Web of Science ®Google Scholar
• Mazini, M., Shirazi, B., & Mahdavi, I. (2019). Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and adaBoost algorithms. Journal of King Saud University-Computer and Information Sciences, 31(4), 541–553. https://doi.org/10.1016/j.jksuci.2018.03.011
   Web of Science ®Google Scholar
• Medjahed, S. A., Saadi, T. A., Benyettou, A., & Ouali, M. (2016). Gray wolf optimizer for hyperspectral band selection. Applied Soft Computing, 40(C), 178–186. https://doi.org/10.1016/j.asoc.2015.09.045
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
   Web of Science ®Google Scholar
• Phan, A. V., Le Nguyen, M., & Bui, L. T. (2017). Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Applied Intelligence, 46(2), 455–469. https://doi.org/10.1007/s10489-016-0843-6
   Web of Science ®Google Scholar
• Raman, M. G., Somu, N., Kirthivasan, K., Liscano, R., & Sriram, V. S. (2017). An efficient intrusion detection system based on hypergraph-genetic algorithm for parameter optimization and feature selection in support vector machine. Knowledge-Based Systems, 134, 1–12. https://doi.org/10.1016/j.knosys.2017.07.005
   Web of Science ®Google Scholar
• Roopa Devi, E. M., & Suganthe, R. C. (2020). Enhanced transductive support vector machine classification with grey wolf optimizer cuckoo search optimization for intrusion detection system. Concurrency and Computation: Practice and Experience, 32(4), e4999. https://doi.org/10.1002/cpe.4999
   Web of Science ®Google Scholar
• Senthilnayaki, B., Venkatalakshmi, K., & Kannan, A. (2015). Intrusion detection using optimal genetic feature selection and SVM based classifier. In 2015 3rd international conference on signal processing, communication and networking (ICSCN) (pp. 1–4). IEEE. https://doi.org/10.1109/ICSCN36207.2015
   Google Scholar
• Seth, J. K., & Chandra, S. (2016). Intrusion detection based on key feature selection using binary GWO. In 2016 3rd international conference on computing for sustainable global development (INDIACom) (pp. 3735–3740). IEEE, New Delhi, India, 16-18 March 2016.
   Google Scholar
• Sheikhpour, R., Sarram, M. A., Gharaghani, S., & Chahooki, M. A. Z. (2017). A survey on semi-supervised feature selection methods. Pattern Recognition, 64(4), 141–158. https://doi.org/10.1016/j.patcog.2016.11.003
   Web of Science ®Google Scholar
• Sheikhpour, R., Sarram, M. A., & Sheikhpour, R. (2016). Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer. Applied Soft Computing, 40(C), 113–131. https://doi.org/10.1016/j.asoc.2015.10.005
   Web of Science ®Google Scholar
• Shunmugapriya, P., & Kanmani, S. (2017). A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC hybrid). Swarm and Evolutionary Computation, 36, 27–36. https://doi.org/10.1016/j.swevo.2017.04.002
   Web of Science ®Google Scholar
• Siedlecki, W., & Sklansky, J. (1993). A note on genetic algorithms for large-scale feature selection. In Handbook of pattern recognition and computer vision (pp. 88–107). https://doi.org/10.1142/9789814343138_0005
   Google Scholar
• Sindhu, S. S. S., Geetha, S., & Kannan, A. (2012). Decision tree based light weight intrusion detection using a wrapper approach. Expert Systems with Applications, 39(1), 129–141. https://doi.org/10.1016/j.eswa.2011.06.013
   Web of Science ®Google Scholar
• Srivastava, D., Singh, R., & Singh, V. (2019). An intelligent gray wolf optimizer: A nature inspired technique in intrusion detection system (IDS). Journal of Advancements in Robotics, 6(1), 18–24. https://www.researchgate.net/profile/Durgesh-Srivastava-4/publication/332800478_An_Intelligent_Gray_Wolf_Optimizer_A_Nature_Inspired_Technique_in_Intrusion_Detection_System_IDS/links/5cca92734585156cd7c1bbd5/An-Intelligent-Gray-Wolf-Optimizer-A-Nature-Inspired-Technique-in-Intrusion-Detection-System-IDS.pdf
   Google Scholar
• Tao, P., Sun, Z., & Sun, Z. (2018). An improved intrusion detection algorithm based on GA and SVM. Ieee Access, 6, 13624–13631. https://doi.org/10.1109/ACCESS.2018.2810198
   Google Scholar
• Tawhid, M. A., & Dsouza, K. B. (2018). Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Applied Computing and Informatics, 16(1/2), 117-136. https://doi.org/10.1016/j.aci.2018.04.001
   Google Scholar
• Thaseen, I. S., & Kumar, C. A. (2017). Intrusion detection model using fusion of chi-square feature selection and multi class SVM. Journal of King Saud University-Computer and Information Sciences, 29(4), 462–472. https://doi.org/10.1016/j.jksuci.2015.12.004
   Web of Science ®Google Scholar
• Too, J., Abdullah, A. R., Mohd Saad, N., Mohd Ali, N., & Tee, W. (2018). A new competitive binary grey wolf optimizer to solve the feature selection problem in EMG signals classification. Computers, 7(4), 58. https://doi.org/10.3390/computers7040058
   Web of Science ®Google Scholar
• Tsai, C. F., Eberle, W., & Chu, C. Y. (2013). Genetic algorithms in feature and instance selection. Knowledge-Based Systems, 39(39), 240–247. https://doi.org/10.1016/j.knosys.2012.11.005
   Web of Science ®Google Scholar
• Usha, M., & Kavitha, P. J. W. N. (2017). Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier. Wireless Networks, 23(8), 2431–2446. https://doi.org/10.1007/s11276-016-1300-5
   Web of Science ®Google Scholar
• Velliangiri, S. (2020). A hybrid BGWO with KPCA for intrusion detection. Journal of Experimental and Theoretical Artificial Intelligence, 32(1), 165–180. https://doi.org/10.1080/0952813X.2019.1647558
   Web of Science ®Google Scholar
• Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
   Google Scholar
• Witten, I. H., Frank, E., Trigg, L. E., Hall, M. A., Holmes, G., & Cunningham, S. J. (1999). Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
   Google Scholar
• Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee Access, 6(C) 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950
   Web of Science ®Google Scholar
• Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2015). A survey on evolutionary computation approaches to feature selection. IEEE Transactions on Evolutionary Computation, 20(4), 606–626. https://doi.org/10.1109/TEVC.2015.2504420
   Web of Science ®Google Scholar
• Xue, Y., Jia, W., Zhao, X., & Pang, W. (2018). An evolutionary computation based feature selection method for intrusion detection. Security and Communication Networks, 2018,1-10. https://doi.org/10.1155/2018/2492956
   Google Scholar
• Yong, Z., Dun-wei, G., & Wan-qiu, Z. (2016). Feature selection of unreliable data using an improved multi-objective PSO algorithm. Neurocomputing, 171(C), 1281–1290. https://doi.org/10.1016/j.neucom.2015.07.057
   Web of Science ®Google Scholar
• Zawbaa, H. M., Emary, E., & Grosan, C. (2016). Feature selection via chaotic antlion optimization. PloS One, 11(3), e0150652. https://doi.org/10.1371/journal.pone.0150652
   PubMed Web of Science ®Google Scholar
• Zawbaa, H. M., Emary, E., Grosan, C., & Snasel, V. (2018, March 1). Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach. Swarm and Evolutionary Computation, 42, 29–42. https://doi.org/10.1016/j.swevo.2018.02.021
   Web of Science ®Google Scholar
• Zeng, D., Wang, S., Shen, Y., & Shi, C. (2017). A GA-based feature selection and parameter optimization for support tucker machine. Procedia Computer Science, 111, 17–23. https://doi.org/10.1016/j.procs.2017.06.004
   Google Scholar
• Zhang, Y., Gong, D., Hu, Y., & Zhang, W. (2015). Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing, 148, 150–157. https://doi.org/10.1016/j.neucom.2012.09.049
   Web of Science ®Google Scholar
• Zhang, Y., Song, X. F., & Gong, D. W. (2017). A return-cost-based binary firefly algorithm for feature selection. Information Sciences, 418(C), 561–574. https://doi.org/10.1016/j.ins.2017.08.047
   Web of Science ®Google Scholar
• Zorarpacı, E., & Özel, S. A. (2016). A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Systems with Applications, 62(C), 91–103. https://doi.org/10.1016/j.eswa.2016.06.004
   Web of Science ®Google Scholar