Open AccessResearch and analysis of course arrangement based on genetic algorithm
Author(s) -
Guangmin Sun,
Yahui Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032050
Subject(s) - crossover , fitness function , coding (social sciences) , computer science , genetic algorithm , algorithm , population based incremental learning , scheduling (production processes) , decimal , mathematical optimization , artificial intelligence , machine learning , mathematics , arithmetic , statistics
Gene coding has always been an important and difficult problem in the course scheduling of genetic algorithm. Starting from the definition of the set and the limited conditions of the scheduling problem, this paper expounds the coding method of the scheduling problem of genetic algorithm in detail. The gene coding method in this paper is a decimal coding with special significance. The information of each course: teaching class, class time, course name, teaching teacher, classroom type and other information are encoded as chromosomes, and the coding method is more standardized and accurate. The crossover rate and variation rate of the designed genetic algorithm will automatically change with the change of fitness, and the self-adapting crossover rate and variation rate can make the algorithm fleetly get the globing greatest result. The fitness function is improved, and the traditional genetic algorithm is compared with the improved genetic algorithm. From the final results, It shows that the improved genetic algorithm is more excellent in efficiency and performance.