Using Genetic Algorithm to Enhance Path Planning for Resource Allocation in Civic and Political Education Courses

In this study, a hybrid genetic algorithm (HGGA) improved based on greedy strategy is proposed for the path planning problem of resource allocation for Civic and Political Education courses.The improved genetic algorithm is proposed based on the greedy strategy to increase the correction operation for perfect and imperfect solutions.Combined with the special needs of Civic and Political Education in value shaping and ideology dissemination, the resource content composition, classification organization and dynamic adaptation mechanism are optimized.Experiments show that HGGA has optimal convergence probability, convergence speed, optimal convergence extreme value and average convergence extreme value in the test function compared with the comparison algorithm, and the solution accuracy is better.The traditional genetic algorithm has to be iterated to about 30 generations to find the optimal solution under the calculation of the algorithm, while the HGGA algorithm can find the optimal solution under the calculation of its algorithm as long as it is iterated to about 23 generations.In the final exam at the end of the teaching experiment, the passing rate of the experimental group class was 7.5% higher than that of the control group class, and the excellence rate was 2.5% higher compared to the control group class.The results of the mean score t-test showed that the mean score of the experimental group was 7.97 points higher than that of the control group at the end of the experiment, and there was a significant difference in the students' performance in Civics and Political Science (p=0.019,p<0.05).The mean scores of students in the experimental group classes were higher than those in the control group classes in the three dimensions of behavioral attitudes, affective tendencies, and value orientations, and significant differences emerged in the dimensions of affective tendencies (p=0.013) and value orientations (p=0.035)(p<0.05).