Improved Genetic Algorithm for English Information Education and Teaching Quality Impact Analysis

Abstract

With the increase of the number of college students, in order to improve the quality of teaching, the professional setting is more and more detailed, the course content is more and more rich, the information teaching system will be increasingly perfect. This paper proposes an improved genetic algorithm for English informatics education scheduling system to address the problem of the contradiction between the rapidly increasing number of students and the limited teaching resources in the current English scheduling in universities. The system first proposes an improved parallel mechanism which computes the probability of population changing evolutionary strategies according to the algebra of population maintaining the optimal solution invariant. The evolution strategy needed by the population can be obtained by fuzzy reasoning according to the evolution degree and individual difference of the population so as to get rid of the local extreme value and search for the global optimal value. Secondly, an improved algorithm initialization method, fitness transformation method, and competition strategy are proposed to adjust the balance between convergence speed and population diversity. Finally, based on Baldwin evolution theory, a new local search strategy is proposed. The algorithm has been tested on famous international data sets and a school’s English curriculum. Experimental results show that this algorithm has higher efficiency and quality than other algorithms with better performance, and the proposed teaching system can improve the teaching quality of English information education.