<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Today’s society is undergoing rapid changes from the information age to the big data age and the intelligence age, and some new requirements and challenges are faced by mathematical modeling curriculum teaching in talent training in the big data age. In this paper, we propose a self-adaptive genetic algorithm scheme for the optimization of a mathematical modeling curriculum system, which is mainly reflected in re-planning and designing the constraints for the optimization of the mathematical modeling curriculum, optimizing and improving the initialization population, selection operator, crossover operator, and variation operator processes. The crossover probability at 0.8, the optimal value of individual fitness and the average value of fitness of the group are both maximum, and the variation probability at 0.01, the optimal value of individual fitness and the average value of fitness of mathematical modeling are both maximum, which indicates that this crossover probability and variation probability are better in the quality of the self-adaptive genetic algorithm. This Study is a guiding reference for the research on the optimization and practical exploration of mathematical modeling curriculum systems in colleges and universities and thus is of historical importance to promote the development of Chinese mathematics.</p>
</abstract>

Today’s society is undergoing rapid changes from the information age to the big data age and the intelligence age, and some new requirements and challenges are faced by mathematical modeling curriculum teaching in talent training in the big data age. In this paper, we propose a self-adaptive genetic algorithm scheme for the optimization of a mathematical modeling curriculum system, which is mainly reflected in re-planning and designing the constraints for the optimization of the mathematical modeling curriculum, optimizing and improving the initialization population, selection operator, crossover operator, and variation operator processes. The crossover probability at 0.8, the optimal value of individual fitness and the average value of fitness of the group are both maximum, and the variation probability at 0.01, the optimal value of individual fitness and the average value of fitness of mathematical modeling are both maximum, which indicates that this crossover probability and variation probability are better in the quality of the self-adaptive genetic algorithm. This Study is a guiding reference for the research on the optimization and practical exploration of mathematical modeling curriculum systems in colleges and universities and thus is of historical importance to promote the development of Chinese mathematics.

Research on the optimization and practice of mathematical modeling curriculum systems based on the perspective of big data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Today’s society is undergoing rapid changes from the information age to the big data age and the intelligence age, and some new requirements and challenges are...

Research on the optimization and practice of mathematical modeling curriculum systems based on the perspective of big data

Published Online: Aug 01, 2023

Page range: -

Received: Sep 29, 2022

Accepted: Feb 09, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00124

Keywords
Big data, Mathematical modeling courses, Genetic algorithms, Crossover probability, Variance probability

© 2023 Jinfeng Jian, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the optimization and practice of mathematical modeling curriculum systems based on the perspective of big data

Published Online: Aug 01, 2023

Page range: -

Received: Sep 29, 2022

Accepted: Feb 09, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00124

KeywordsBig data, Mathematical modeling courses, Genetic algorithms, Crossover probability, Variance probability

© 2023 Jinfeng Jian, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Big data, Mathematical modeling courses, Genetic algorithms, Crossover probability, Variance probability