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<p>In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of the project to help students solve complex problems. The traditional adaptive differential evolution algorithm has the problems of local optimum and low convergence accuracy; to overcome these limitations, this paper proposes an enhanced reverse learning adaptive differential evolution algorithm. The OL-ADE algorithm is improved by introducing the reverse learning strategy in machine learning to extract the reverse elite individual population from the original individual population and select the better-adapted individuals for mutation operation. After the project-based teaching, the students’ works have high scores in layout color matching, technical operation, organization, information awareness, and information social responsibility, and their scores are 8.6, 9.75, 9, 9.35, and 8.75, respectively. Therefore, this paper proves the Adaptive Difference Evolution algorithm’s effectiveness in innovative teaching of high school project-based courses.</p>
</abstract>

In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of the project to help students solve complex problems. The traditional adaptive differential evolution algorithm has the problems of local optimum and low convergence accuracy; to overcome these limitations, this paper proposes an enhanced reverse learning adaptive differential evolution algorithm. The OL-ADE algorithm is improved by introducing the reverse learning strategy in machine learning to extract the reverse elite individual population from the original individual population and select the better-adapted individuals for mutation operation. After the project-based teaching, the students’ works have high scores in layout color matching, technical operation, organization, information awareness, and information social responsibility, and their scores are 8.6, 9.75, 9, 9.35, and 8.75, respectively. Therefore, this paper proves the Adaptive Difference Evolution algorithm’s effectiveness in innovative teaching of high school project-based courses.

Adaptive Differential Evolutionary Algorithm for Innovative Teaching and Learning in High School Project-Based Courses

Applied Mathematics and Nonlinear Sciences

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

In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of...

Adaptive Differential Evolutionary Algorithm for Innovative Teaching and Learning in High School Project-Based Courses

Publié en ligne: 08 nov. 2023

Pages: -

Reçu: 02 févr. 2023

Accepté: 19 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01026

Mots clés
Adaptive differential evolution, Inverse learning, OL-ADE algorithm, Machine learning, Project-based instruction

© 2023 Liangliang Hu, published by Sciendo

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

Adaptive Differential Evolutionary Algorithm for Innovative Teaching and Learning in High School Project-Based Courses

Publié en ligne: 08 nov. 2023

Pages: -

Reçu: 02 févr. 2023

Accepté: 19 mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01026

Mots clésAdaptive differential evolution, Inverse learning, OL-ADE algorithm, Machine learning, Project-based instruction

© 2023 Liangliang Hu, published by Sciendo

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

Mots clés
Adaptive differential evolution, Inverse learning, OL-ADE algorithm, Machine learning, Project-based instruction