{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:51:29Z","timestamp":1773370289564,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The most important aspect of a genetic algorithm (GA) lies in the optimal solution found. The result obtained by a genetic algorithm can be evaluated according to the quality of this solution. It is important that this solution is optimal or close to optimal in relation to the defined performance criteria, usually the fitness value. This study addresses the problem of automated generation of assessment tests in education. In this paper, we present the design of a model of assessment test generation used in education using genetic algorithms. The assessment covers a series of courses taught over a period of time. The genetic algorithm presents an improvement or development, which consists of the initial population variation, obtained by the selection of a large fixed number of individuals from various populations, which are ordered by the fitness value using merge sort, chosen for the reason of the high number of individuals. The initial population variation can be seen as a specific modality for increasing the diversity and number of the initial population of a genetic algorithm, which influences the algorithm performance. This process increases the diversity and quality of the initial population, improving the algorithm\u2019s overall performance. The development\/novelty brought about by this paper is related to its application to a specific issue (educational assessment test generation) and the specific methodology used for population variation. This development can be applied for large sets of individuals, the variety, and the large number of generated individuals leading to higher odds to increase the performance of the algorithm. Experimental results demonstrate that the proposed method outperforms traditional GA implementations in terms of solution quality and convergence speed, showing its effectiveness for large-scale test generation tasks.<\/jats:p>","DOI":"10.3390\/bdcc9040098","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T08:11:39Z","timestamp":1744704699000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Enhanced Genetic Algorithm for Optimized Educational Assessment Test Generation Through Population Variation"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1389-7684","authenticated-orcid":false,"given":"Doru-Anastasiu","family":"Popescu","sequence":"first","affiliation":[{"name":"Pitesti University Center, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","unstructured":"Bertsimas, D., and Tsitsiklis, J.N. (1997). Introduction to Linear Optimization, Athena Scientific."},{"key":"ref_2","unstructured":"Nemhauser, G.L., and Wolsey, L.A. (1999). Integer and Combinatorial Optimization, Wiley-Interscience."},{"key":"ref_3","unstructured":"Jackson, P. (1998). Introduction to Expert Systems, Addison-Wesley Longman Publishing Co., Inc.. [3rd ed.]."},{"key":"ref_4","unstructured":"Giarratano, J.C., and Riley, G.D. (2005). Expert Systems: Principles and Programming, Brooks\/Cole Publishing Co."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., and Shapira, B. (2022). Recommender Systems Handbook, Springer. [3rd ed.].","DOI":"10.1007\/978-1-0716-2197-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Future Directions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_7","unstructured":"Mitchell, T.M. (1997). Machine Learning, McGraw-Hill."},{"key":"ref_8","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_9","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/4235.985692","article-title":"The Particle Swarm\u2014Explosion, Stability, and Convergence in a Multidimensional Complex Space","volume":"6","author":"Clerc","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_11","unstructured":"Aarts, E.H.L., and Lenstra, J.K. (1997). Local Search in Combinatorial Optimization, John Wiley & Sons."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by Simulated Annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_13","first-page":"115","article-title":"Simulated binary crossover for continuous search space","volume":"9","author":"Deb","year":"1995","journal-title":"Complex Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_15","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press."},{"key":"ref_16","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dorigo, M., and St\u00fctzle, T. (2004). Ant Colony Optimization, MIT Press.","DOI":"10.7551\/mitpress\/1290.001.0001"},{"key":"ref_18","unstructured":"Blumer, A., and Karp, R.M. (2003, January 23\u201326). Ant Algorithms for Network Routing. Proceedings of the International Conference on Networking and Services, Las Vegas, NV, USA."},{"key":"ref_19","unstructured":"Popescu, D.A., Stanciu, G.C., and Nijloveanu, D. (2021, January 7\u201311). Evaluation Test Generator Using a List of Keywords. Proceedings of the Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event. Proceedings 17."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.3233\/JIFS-219271","article-title":"Study on the efficiency of the design of the drip irrigation management system using plastics","volume":"43","author":"Victor","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_21","first-page":"169","article-title":"Elaboration of some models to reduce the hydric erosion in Olt county","volume":"12","author":"Nijloveanu","year":"2012","journal-title":"Econ. Eng. Agric. Rural Dev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Thakkar, A., and Chaudhari, K. (2024). Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade. Comput. Sci. Rev., 53.","DOI":"10.1016\/j.cosrev.2024.100652"},{"key":"ref_23","first-page":"899","article-title":"Comparative insights into labour productivity trends in the european union\u2019s agri-food sector","volume":"24","author":"Smedescu","year":"2024","journal-title":"Sci. Pap. Ser. Manag. Econ. Eng. Agric. Rural Dev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A Review on Genetic Algorithm: Past, Present, and Future","volume":"80","author":"Chahar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Angelova, M., and Pencheva, T. (2011). Tuning Genetic Algorithm Parameters to Improve Convergence Time. Int. J. Chem. Eng., 2011.","DOI":"10.1155\/2011\/646917"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.asoc.2014.08.025","article-title":"A comparative review of approaches to prevent premature convergence in GA","volume":"24","author":"Pandey","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1080\/00207543.2013.784411","article-title":"Using response surface design to determine the optimal parameters of genetic algorithm and a case study","volume":"51","author":"Yaman","year":"2013","journal-title":"Int. J. Prod. Res."},{"key":"ref_28","first-page":"211","article-title":"A new mutation operator for real coded genetic algorithms","volume":"193","author":"Deep","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_29","unstructured":"Ono, I. (1997, January 19\u201323). A real-coded genetic algorithms for function optimization using unimodal normal distribution crossover. Proceedings of the Seventh International Conference on Genetic Algorithms: Michigan State University, East Lansing, MI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, K., Li, Y., Murray-Smith, D., and Sharman, K. (1995, January 12\u201314). System Identification And Linearisation Using Genetic Algorithms With Simulated Annealing. Proceedings of the First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK.","DOI":"10.1049\/cp:19951043"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hedar, A.R., and Fukushima, M. (2003). Simplex Coding Genetic Algorithm for the Global Optimization of Nonlinear Functions, Springer.","DOI":"10.1007\/978-3-540-36510-5_17"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1016\/j.asoc.2012.11.003","article-title":"A simplex crossover based evolutionary algorithm including the genetic diversity as objective","volume":"13","author":"Benini","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_33","unstructured":"Richter, J.N., and Peak, D. (2002, January 12\u201317). Fuzzy evolutionary cellular automata. Proceedings of the International Conference on Artificial Neural Networks In Engineering, Honolulu, HI, USA."},{"key":"ref_34","unstructured":"Harik, G.R., and Lobo, F.G. (1999, January 13\u201317). A parameter-less genetic algorithm. Proceedings of the GECCO, Orlando, FL, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, L., Ong, Y.S., and Gupta, A. (2019). Genetic algorithm and its advances in embracing memetics. Evolutionary and Swarm Intelligence Algorithms, Springer.","DOI":"10.1007\/978-3-319-91341-4_5"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wan, W., and Birch, J.B. (2013). An improved hybrid genetic algorithm with a new local search procedure. J. Appl. Math., 2013.","DOI":"10.1155\/2013\/103591"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yusoff, M., and Roslan, N. (2019, January 26\u201330). Evaluation of genetic algorithm and hybrid genetic algorithm-hill climbing with elitist for lecturer university timetabling problem. Proceedings of the Advances in Swarm Intelligence: 10th International Conference, ICSI 2019, Chiang Mai, Thailand. Proceedings, Part I 10.","DOI":"10.1007\/978-3-030-26369-0_34"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ins.2020.08.040","article-title":"GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems","volume":"547","author":"Palmieri","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ross, B.J. (2019). A Lamarckian evolution strategy for genetic algorithms. Practical Handbook of Genetic Algorithms, CRC Press.","DOI":"10.1201\/9780429128356-1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"19532","DOI":"10.1109\/ACCESS.2024.3361399","article-title":"Traffic Improvement in Manhattan Road Networks With the Use of Parallel Hybrid Biobjective Genetic Algorithm","volume":"12","author":"Akopov","year":"2024","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/09528139008953723","article-title":"Genitor II: A distributed genetic algorithm","volume":"2","author":"Whitley","year":"1990","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1142\/S0218001496000438","article-title":"Genetic algorithm with elitist model and its convergence","volume":"10","author":"Bhandari","year":"1996","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TEVC.2003.814633","article-title":"Elitism-based compact genetic algorithms","volume":"7","author":"Ahn","year":"2003","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"44531","DOI":"10.1109\/ACCESS.2018.2861760","article-title":"Elitism and distance strategy for selection of evolutionary algorithms","volume":"6","author":"Du","year":"2018","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.1016\/j.ins.2007.02.032","article-title":"Population variation in genetic programming","volume":"177","author":"Kouchakpour","year":"2007","journal-title":"Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.envsoft.2014.08.023","article-title":"Using characteristics of the optimisation problem to determine the genetic algorithm population size when the number of evaluations is limited","volume":"69","author":"Gibbs","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.ins.2008.12.009","article-title":"Dynamic population variation in genetic programming","volume":"179","author":"Kouchakpour","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1093\/bioinformatics\/17.2.137","article-title":"The massively parallel genetic algorithm for RNA folding: MIMD implementation and population variation","volume":"17","author":"Shapiro","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_49","first-page":"1","article-title":"Genetic algorithm performance with different selection strategies in solving TSP","volume":"Volume 2","author":"Razali","year":"2011","journal-title":"Proceedings of the World Congress on Engineering"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/01969729308961696","article-title":"Genetic algorithms: What fitness scaling is optimal?","volume":"24","author":"Kreinovich","year":"1993","journal-title":"Cybern. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chehouri, A., Younes, R., Khoder, J., Perron, J., and Ilinca, A. (2017). A selection process for genetic algorithm using clustering analysis. Algorithms, 10.","DOI":"10.3390\/a10040123"},{"key":"ref_52","unstructured":"Xinping, L., and Ying, L. (2009, January 15\u201317). Adaptive Genetic Algorithm Based on Population Diversity. Proceedings of the 2009 International Forum on Information Technology and Applications, Chengdu, China."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hook, D.W., Porter, S.J., and Herzog, C. (2018). Dimensions: Building Context for Search and Evaluation. Front. Res. Metrics Anal., 3.","DOI":"10.3389\/frma.2018.00023"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/07317131.2018.1425352","article-title":"VOSviewer","volume":"35","author":"Wong","year":"2018","journal-title":"Tech. Serv. Q."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Basiouni, A., and Frasson, C. (2024). Analyzing the Performance of Distributed Web Systems Within an Educational Assessment Framework. Breaking Barriers with Generative Intelligence, Springer. Using GI to Improve Human Education and Well-Being.","DOI":"10.1007\/978-3-031-65996-6"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/98\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:59Z","timestamp":1760029979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/98"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":55,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040098"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040098","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]}}}