Research on Teacher Education Curriculum Design Combined with Genetic Algorithm Optimization from the Perspective of Basic Theory of Education

Abstract

This paper is based on the perspective of basic educational theory and proposes a course design scheme based on a combinatorial genetic algorithm to address the current issues in higher education teacher education course design. First, based on the relevant factors and constraints of course design problems, the objective function, mathematical model, and fitness function for this study are determined. To address the local optimality issue in traditional genetic algorithms, the simulated annealing algorithm (SA) is introduced to optimize the genetic algorithm, thereby completing the course design work for teacher education in higher education institutions. Numerical experiments are then conducted to conduct in-depth validation and analysis of the course design for teacher education. After introducing the simulated annealing algorithm (SA), the convergence speed of the genetic algorithm (GA) was significantly improved. After 100 training runs, the algorithm could obtain the optimal solution to the course design problem, verifying the optimization effect of the simulated annealing algorithm on the genetic algorithm. Additionally, under the influence of the algorithm in this paper, the satisfaction rate of student course selection remained at 100%, demonstrating the practical application value of this algorithm in teacher education course design.