Abstract
Personalized courseware is an important research field based on computer multimedia, because fixed courseware is not suitable for all students. The purpose of this paper is to study the mathematical model of genetic algorithm in the pattern theorem of computer multimedia courseware. Using the genetic algorithm of pattern theory, the mathematical model corresponding to the problem of multimedia courseware is designed and constructed. The traditional genetic algorithm is improved and verified by experiments. The improved algorithm is applied to the final paper output module system of multimedia computer. Taking the students of the course data structure in the Institute of information automation as the experimental object, a personalized multimedia computer program based on genetic algorithm is developed by designing the motion function. The experimental results show that if we continue to complete the remaining iterations on the premise of reaching the fitness value, the fitness value of the test paper generated by snga will be closer to the optimal value of the fitness function. After selecting the personalized multimedia courseware on the computer, the number of people who got higher scores in the post test than in the same pre-test was 64.
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References
Bonito, S.R.: The usefulness of case studies in a virtual clinical environment (VCE) multimedia courseware in nursing. J. Med. Invest. 66(1.2), 38–41 (2019)
Fu, H., Fu, W.: Research on the influence of multimedia on Chinese teaching in senior high school. World Sci. Res. J. 6(5), 86–94 (2020)
Tsai, S.C.: Implementing interactive courseware into EFL business writing: computational assessment and learning satisfaction. Interact. Learn. Environ. 27(1–4), 46–61 (2019)
Gong, D.W., Sun, J., Miao, Z.: A set-based genetic algorithm for interval many-objective optimization problems. IEEE Trans. Evol. Comput. 22(99), 47–60 (2018)
Aziza, H., Krichen, S.: Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing. Computing 100(2), 65–91 (2018)
Latheef, G.: Comparative analysis of ant colony optimization and genetic algorithm on solving symmetrical travelling salesman problem. J. Adv. Res. Dyn. Control Syst. 12(SP7), 2629–2635 (2020)
Pandey, K., Kumar, S., Malik, A., et al.: Artificial neural network optimized with a genetic algorithm for seasonal groundwater table depth prediction in Uttar Pradesh, India. Sustainability 12(8932), 1–24 (2020)
Harpale, V., Bairagi, V.: FPGA based architecture implementation for epileptic seizure detection using one way ANOVA and genetic algorithm. Biomed. Pharmacol. J. 12(3), 1543–1553 (2019)
Syarif, A., Anggraini, D., Muludi, K., et al.: Comparing various genetic algorithm approaches for multiple-choice multi-dimensional knapsack problem (mm-KP). Int. J. Intell. Eng. Syst. 13(5), 455–462 (2020)
Majeed, A.M.A.: Optimal power flow based on bird swarm optimization and genetic algorithm. J. Eng. Appl. Sci. 14(21), 8034–8038 (2019)
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Huang, S. (2021). Computer Multimedia Courseware in Genetic Algorithm Mathematical Model of Pattern Theorem. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2021. Advances in Intelligent Systems and Computing, vol 1384. Springer, Cham. https://doi.org/10.1007/978-3-030-74811-1_42
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DOI: https://doi.org/10.1007/978-3-030-74811-1_42
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