{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:45:37Z","timestamp":1774370737595,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Malaysian government","award":["GUP-2020-063"],"award-info":[{"award-number":["GUP-2020-063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.<\/jats:p>","DOI":"10.3390\/sym12111758","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T08:59:28Z","timestamp":1603443568000},"page":"1758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":244,"title":["Genetic Algorithm Based on Natural Selection Theory for Optimization Problems"],"prefix":"10.3390","volume":"12","author":[{"given":"Musatafa Abbas","family":"Albadr","sequence":"first","affiliation":[{"name":"CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"given":"Sabrina","family":"Tiun","sequence":"additional","affiliation":[{"name":"CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5157-7921","authenticated-orcid":false,"given":"Masri","family":"Ayob","sequence":"additional","affiliation":[{"name":"CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"given":"Fahad","family":"AL-Dhief","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Department of Communication Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor 81310, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10951-013-0352-y","article-title":"An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling","volume":"17","author":"Alzaqebah","year":"2013","journal-title":"J. Sched."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.cor.2014.09.005","article-title":"Hybrid bee colony optimization for examination timetabling problems","volume":"54","author":"Alzaqebah","year":"2015","journal-title":"Comput. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6755","DOI":"10.1007\/s00500-016-2225-8","article-title":"An adaptive guided variable neighborhood search based on honey-bee mating optimization algorithm for the course timetabling problem","volume":"21","author":"Aziz","year":"2016","journal-title":"Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.ejor.2011.08.006","article-title":"A honey-bee mating optimization algorithm for educational timetabling problems","volume":"216","author":"Sabar","year":"2012","journal-title":"Eur. J. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.ins.2014.08.050","article-title":"Multi-population cooperative bat algorithm-based optimization of artificial neural network model","volume":"294","author":"Jaddi","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ipl.2015.08.001","article-title":"A solution representation of genetic algorithm for neural network weights and structure","volume":"116","author":"Jaddi","year":"2016","journal-title":"Inf. Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.asoc.2015.12.032","article-title":"Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units","volume":"41","author":"Carvalho","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.asoc.2013.08.011","article-title":"MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier","volume":"14","author":"Hassanien","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1016\/j.asoc.2013.01.025","article-title":"Honey bee behavior inspired load balancing of tasks in cloud computing environments","volume":"13","author":"Krishna","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Albadr, M.A.A., and Tiun, S. (2020). Spoken Language Identification Based on Particle Swarm Optimisation\u2013Extreme Learning Machine Approach. Circuits Syst. Signal. Process., 1\u201327.","DOI":"10.1007\/s00034-020-01388-9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s10772-019-09621-w","article-title":"Spoken language identification based on optimised genetic algorithm\u2013extreme learning machine approach","volume":"22","author":"Albadr","year":"2019","journal-title":"Int. J. Speech Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1550021","DOI":"10.1142\/S0218213015500219","article-title":"A Hybrid Meta-Heuristic Algorithm for Vehicle Routing Problem with Time Windows","volume":"24","author":"Yassen","year":"2015","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_13","first-page":"43","article-title":"The Effect of Hybridizing Local Search Algorithms with Harmony Search for the Vehicle Routing Problem with Time Windows","volume":"73","author":"Yassen","year":"2015","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ins.2015.07.009","article-title":"Meta-harmony search algorithm for the vehicle routing problem with time windows","volume":"325","author":"Yassen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_15","first-page":"14","article-title":"Nature-Inspired Algorithms: State-of-Art, Problems and Prospects","volume":"100","author":"Agarwal","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.engappai.2017.09.012","article-title":"Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting","volume":"67","author":"Jaddi","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"1","author":"Poli","year":"2007","journal-title":"Swarm Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/003754970107600201","article-title":"A New Heuristic Optimization Algorithm: Harmony Search","volume":"76","author":"Geem","year":"2001","journal-title":"Simulation"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.cnsns.2016.06.006","article-title":"Kidney-inspired algorithm for optimization problems","volume":"42","author":"Jaddi","year":"2017","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1022602019183","article-title":"Genetic algorithms and machine learning","volume":"3","author":"Goldberg","year":"1988","journal-title":"Mach. Learn."},{"key":"ref_23","first-page":"1482","article-title":"Genetic algorithms","volume":"7","author":"Holland","year":"2012","journal-title":"Sci. Am."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mirjalili, S. (2019). Genetic algorithm. Evolutionary Algorithms and Neural Networks, Springer.","DOI":"10.1007\/978-3-319-93025-1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Contreras-Bolton, C., and Parada, V. (2015). Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137724"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Anam, S. (2019). Parameters Estimation of Enzymatic Reaction Model for Biodiesel Synthesis by Using Real Coded Genetic Algorithm with Some Crossover Operations, IOP Publishing.","DOI":"10.1088\/1757-899X\/546\/5\/052006"},{"key":"ref_27","first-page":"335","article-title":"A Study of Genetic Algorithm and Crossover Techniques","volume":"8","author":"Malik","year":"2019","journal-title":"Int. J. Comput. Sci. Mob. Comput."},{"key":"ref_28","unstructured":"Mankad, K.B. (2013). A Genetic Fuzzy Approach to Measure Multiple Intelligence, Sardar Patel University."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Albadr, M.A.A., Tiun, S., Al-Dhief, F.T., and Sammour, M.A.M. (2018). Spoken language identification based on the enhanced self-adjusting extreme learning machine approach. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194770"},{"key":"ref_30","unstructured":"Holland, J.H. (1975). Adaption in Natural and Artificial Systems. An Introductory Analysis with Application to Biology, Control and Artificial Intelligence, The University of Michigan. [1st ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1016\/j.neucom.2010.01.023","article-title":"Deterministic local alignment methods improved by a simple genetic algorithm","volume":"73","author":"Bi","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3180","DOI":"10.1016\/j.neucom.2011.04.009","article-title":"Rules extraction from constructively trained neural networks based on genetic algorithms","volume":"74","author":"Mohamed","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_33","first-page":"134","article-title":"Genetic algorithms for lens design: A review","volume":"48","author":"Lakshminarayanan","year":"2018","journal-title":"J. Opt."},{"key":"ref_34","first-page":"71","article-title":"Genetic Algorithms + Data Structures = Evolution Programs","volume":"18","author":"Michalewicz","year":"1996","journal-title":"Math. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, F., Fu, X., Li, H., and Dong, G. (2016, January 15\u201317). Improved Roulette Wheel Selection-Based Genetic Algorithm for TSP. Proceedings of the 2016 International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, China.","DOI":"10.1109\/ICNISC.2016.041"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.jvcir.2018.12.012","article-title":"Face recognition based on genetic algorithm","volume":"58","author":"Zhi","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.ejor.2018.07.012","article-title":"A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar","volume":"272","author":"Zhang","year":"2019","journal-title":"Eur. J. Oper. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"25259","DOI":"10.1007\/s11042-020-09191-z","article-title":"Cryptanalysis of genetic algorithm-based encryption scheme","volume":"79","author":"Wong","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ahmed, R., Zayed, T., and Nasiri, F. (2020). A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities. Algorithms, 13.","DOI":"10.3390\/a13090210"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kar, S., and Kabir, M.M.J. (2019, January 7\u20139). Comparative Analysis of Mining Fuzzy Association Rule using Genetic Algorithm. Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019sBazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679336"},{"key":"ref_41","first-page":"829","article-title":"Multi-attribute intelligent decision-making method based on triangular fuzzy number hesitant intuitionistic fuzzy sets","volume":"39","author":"Tan","year":"2017","journal-title":"Syst. Eng. Electron."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"348","DOI":"10.4218\/etrij.2018-0254","article-title":"Genetic algorithm-based content distribution strategy for F- RAN architectures","volume":"41","author":"Li","year":"2019","journal-title":"ETRI J."},{"key":"ref_43","first-page":"372","article-title":"Genetic algorithm\/extreme learning machine paradigm for cancer detection","volume":"46","author":"Serbanescu","year":"2019","journal-title":"Ann. Univ. Craiova Math. Comput. Sci. Ser."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Choudhary, A., Kumar, M., Gupta, M.K., Unune, D.K., and Mia, M. (2019). Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms. Neural Comput. Appl., 1\u201314.","DOI":"10.1007\/s00521-019-04404-5"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.applthermaleng.2018.10.070","article-title":"Using PSO-GA algorithm for training artificial neural network to forecast solar space heating system parameters","volume":"147","author":"Jamali","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lipare, A., Edla, D.R., Cheruku, R., and Tripathi, D. (2020). GWO-GA Based Load Balanced and Energy Efficient Clustering Approach for WSN. Smart Trends in Computing and Communications, Springer.","DOI":"10.1007\/978-981-15-0077-0_29"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Beg, A.H., and Islam, Z. (2016, January 24\u201329). Novel crossover and mutation operation in genetic algorithm for clustering. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744049"},{"key":"ref_48","first-page":"34","article-title":"Crossover Operators in Genetic Algorithms: A Review","volume":"162","author":"Kora","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_49","unstructured":"Darwin, C., and Wallace, A.R. (1958). Evolution by Natural Selection, Cambridge University Press."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"937","DOI":"10.2307\/2485224","article-title":"On the Origin of Species by Means of Natural Selection","volume":"49","author":"Livezey","year":"1953","journal-title":"Am. Midl. Nat."},{"key":"ref_51","first-page":"150","article-title":"A literature survey of benchmark functions for global optimisation problems","volume":"4","author":"Jamil","year":"2013","journal-title":"Int. J. Math. Model. Numer. Optim."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.swevo.2018.02.013","article-title":"A novel nature-inspired algorithm for optimization: Squirrel search algorithm","volume":"44","author":"Jain","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Alexander, V., and Annamalai, P. (2015). An Elitist Genetic Algorithm Based Extreme Learning Machine. Softw. Eng. Intell. Syst., 301\u2013309.","DOI":"10.1007\/978-981-10-0251-9_29"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1007\/s00521-015-2010-0","article-title":"Comparison of modified teaching\u2013learning-based optimization and extreme learning machine for classification of multiple power signal disturbances","volume":"27","author":"Nayak","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s11063-016-9496-z","article-title":"A Kind of Parameters Self-adjusting Extreme Learning Machine","volume":"44","author":"Niu","year":"2016","journal-title":"Neural Process. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11571-015-9358-9","article-title":"A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training","volume":"10","author":"Yang","year":"2015","journal-title":"Cogn. Neurodyn."},{"key":"ref_58","first-page":"4610","article-title":"Extreme learning machine: A review","volume":"12","author":"Albadra","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation, Springer.","DOI":"10.1007\/11941439_114"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/11\/1758\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:42Z","timestamp":1760178402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/11\/1758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,23]]},"references-count":59,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["sym12111758"],"URL":"https:\/\/doi.org\/10.3390\/sym12111758","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,23]]}}}