{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:07:03Z","timestamp":1772906823239,"version":"3.50.1"},"reference-count":45,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2019,9,14]],"date-time":"2019-09-14T00:00:00Z","timestamp":1568419200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely,\n                    <jats:italic>k<\/jats:italic>\n                    nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.\n                  <\/jats:p>","DOI":"10.1515\/jisys-2019-0062","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T05:01:36Z","timestamp":1568610096000},"page":"1598-1610","source":"Crossref","is-referenced-by-count":35,"title":["Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection"],"prefix":"10.1515","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2954-9876","authenticated-orcid":false,"given":"Manosij","family":"Ghosh","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1375-777X","authenticated-orcid":false,"given":"Ritam","family":"Guha","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]},{"given":"Imran","family":"Alam","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]},{"given":"Priyank","family":"Lohariwal","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]},{"given":"Devesh","family":"Jalan","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8813-4086","authenticated-orcid":false,"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department , Jadavpur University , 188, Raja S.C. Mallick Road , Kolkata 700032, West Bengal , India"}]}],"member":"374","published-online":{"date-parts":[[2019,9,14]]},"reference":[{"key":"2025120523341675514_j_jisys-2019-0062_ref_001","doi-asserted-by":"crossref","unstructured":"L. M. Q. Abualigah, Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering, in: Studies in Computational Intelligence, vol. 816, Springer, Cham, 2019.","DOI":"10.1007\/978-3-030-10674-4"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_002","doi-asserted-by":"crossref","unstructured":"L. M. Q. Abualigah and E. S. Hanandeh, Applying genetic algorithms to information retrieval using vector space model, Int. J. Comput. Sci. Eng. 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Talbi, Gene selection in cancer classification using PSO\/SVM and GA\/SVM hybrid algorithms, in: 2007 IEEE Congress on Evolutionary Computation, Singapore, pp. 284\u2013290, 2007.","DOI":"10.1109\/CEC.2007.4424483"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_009","doi-asserted-by":"crossref","unstructured":"M. E. Basiri and S. Nemati, A novel hybrid ACO-GA algorithm for text feature selection, in: 2009 IEEE Congress on Evolutionary Computation, Trondheim, pp. 2561\u20132568, 2009.","DOI":"10.1109\/CEC.2009.4983263"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_010","doi-asserted-by":"crossref","unstructured":"H. Ceylan and M. G. H. Bell, Traffic signal timing optimisation based on genetic algorithm approach, including drivers\u2019 routing,Transport. Res. 38 (2004), 329\u2013342.","DOI":"10.1016\/S0191-2615(03)00015-8"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_011","unstructured":"J. Culberson, On the futility of blind search, in: Technical Report 96-19, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada, July 1996."},{"key":"2025120523341675514_j_jisys-2019-0062_ref_012","doi-asserted-by":"crossref","unstructured":"B. Dengiz, F. Altiparmak and A. E. Smith, Local search genetic algorithm for optimal design of reliable networks, IEEE Trans. Evol. Comput. 1 (1997), 179\u2013188.","DOI":"10.1109\/4235.661548"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_013","doi-asserted-by":"crossref","unstructured":"M. Dorigo and M. Birattari, Ant Colony Optimization, in: C. Sammut and G. I. Webb, eds., Encyclopedia of Machine Learning, Springer, Boston, MA, 2011.","DOI":"10.1007\/978-0-387-30164-8_22"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_014","unstructured":"D. Dua and C. 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Fedosov, Greedy heuristic algorithm for solving series of eee components classification problem, in: IOP Conf. Ser. Mater. Sci. Eng., 2016.","DOI":"10.1088\/1757-899X\/122\/1\/012011"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_030","doi-asserted-by":"crossref","unstructured":"J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, in: 1997 IEEE Int. Conf. Syst. Man, Cybern. Comput. Cybern. Simul., IEEE, pp. 4104\u20134108, 1997.","DOI":"10.1109\/ICSMC.1997.637339"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_031","doi-asserted-by":"crossref","unstructured":"J. T. Kent, Information gain and a general measure of correlation, Biometrika. 70 (1983), 163\u2013173.","DOI":"10.1093\/biomet\/70.1.163"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_032","doi-asserted-by":"crossref","unstructured":"R. Leardi, Application of genetic algorithm \u2013 PLS for feature selection in spectral data sets, J. 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Biswas and C. K. Jain, SVM classifier based feature selection using GA, ACO and PSO for siRNA design, in: Advances in Swarm Intelligence, pp. 307\u2013314, Springer, Berlin, 2010.","DOI":"10.1007\/978-3-642-13498-2_40"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_036","unstructured":"Problem-specific knowledge in heuristics. 2016. http:\/\/antor.uantwerpen.be\/problem-specific-knowledge-in-heuristics\/ (accessed January 7, 2019)."},{"key":"2025120523341675514_j_jisys-2019-0062_ref_037","doi-asserted-by":"crossref","unstructured":"E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, GSA: a gravitational search algorithm, Inf. Sci. (NY). 179 (2009), 2232\u20132248.","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_038","doi-asserted-by":"crossref","unstructured":"M. Sheikhan and N. Mohammadi, Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection, Neural. Comput. Appl. 21 (2012), 1961\u20131970.","DOI":"10.1007\/s00521-011-0599-1"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_039","unstructured":"J. Sun, B. Feng and W. Xu, Particle swarm optimization with particles having quantum behavior, in: Proc. 2004 Congr. Evol. Comput. (IEEE Cat. No. 04TH8753), pp. 325\u2013331, IEEE, Portland, OR, USA, 2004."},{"key":"2025120523341675514_j_jisys-2019-0062_ref_040","doi-asserted-by":"crossref","unstructured":"R. J. Tallarida and R. B. Murray, Chi-square test, in: Man. Pharmacol. Calc., pp. 140\u2013142, Springer, New York, NY, 1987.","DOI":"10.1007\/978-1-4612-4974-0_43"},{"key":"2025120523341675514_j_jisys-2019-0062_ref_041","doi-asserted-by":"crossref","unstructured":"P. J. Van Laarhoven and E. H. 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