scholarly journals Design and Application of an Improved Genetic Algorithm to a Class Scheduling System

2021 ◽  
Vol 16 (01) ◽  
pp. 44
Author(s):  
Xiangliu Chen ◽  
Xiao-Guang Yue ◽  
Rita Yi Man Li ◽  
Ainur Zhumadillayeva ◽  
Ruru Liu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Strong Support ◽  
Colleges And Universities ◽  
Hill Climbing ◽  
Teaching Management ◽  
Scheduling System ◽  
Course Scheduling ◽  
Class Scheduling ◽  
Classroom Resources

The current expansion of national colleges and universities or the increase in the number of enrolments requires teaching management to ensure the quality of teaching. The problem of scheduling is a very complicated prob-lem in teaching management, and there are many restrictions. If the number of courses scheduled is large, it will be necessary to repeat the experiment and make adjustments. This kind of work is difficult to accomplish accu-rately by manpower. Moreover, for a comprehensive university, there are many subjects, many professional settings, limited classroom resources, limited multimedia classroom resources, and other factors that limit and constrain the results of class scheduling. Such a large data volume and com-plicated workforce are difficult to complete accurately. Therefore, manpow-er scheduling cannot meet the needs of the educational administration of colleges and universities. Today, computer technology is highly developed. It is very economical to use software technology to design a course schedul-ing system and let the computer complete this demanding and rigorous work. Common course scheduling systems mainly include hill climbing al-gorithms, tabu search algorithms, ant colony algorithms, and simulated an-nealing algorithms. These algorithms have certain shortcomings. In this re-search, we investigated the mutation genetic algorithm and applied the algo-rithm to the student’s scheduling system. Finally, we tested the running speed and accuracy of the system. We found that the algorithm worked well in the course scheduling system and provided strong support for solving the tedious scheduling work of the educational administration staff.

scholarly journals Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

2018 ◽  
Vol 13 (06) ◽  
pp. 29
Author(s):  
Wang Wen-jing
Keyword(s):  
Genetic Algorithm ◽  
Model Building ◽  
Colleges And Universities ◽  
Adaptive Genetic Algorithm ◽  
Initial Population ◽  
Teaching Management ◽  
Course Scheduling ◽  
Individual Fitness ◽  
Comparison Results ◽  
Hard And Soft Constraints

Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

scholarly journals Automatic Course Scheduling System in Universities Based on Hybrid Genetic-Ant Colony Algorithm

2021 ◽  
Vol 2066 (1) ◽  
pp. 012079
Author(s):  
Yanbei Duan ◽  
Wenjie Lu
Keyword(s):  
Hybrid Algorithm ◽  
Ant Colony Algorithm ◽  
Operation Time ◽  
Original System ◽  
Colleges And Universities ◽  
Ant Colony ◽  
Scheduling System ◽  
Course Scheduling ◽  
System A ◽  
New System

Abstract Scheduling is the daily work of the Ministry of Education in Colleges and universities. In the past ten years, the scale of our colleges and universities has expanded rapidly, but the teaching resources are relatively limited. Many schools are facing the problem of insufficient classroom resources and teachers resources. The current way of organizing courses is increasingly difficult to make full use of existing resources to solve the changing needs and inefficiencies, which need to be improved urgently. This paper applies the hybrid Genetic-Ant algorithm to the automatic course scheduling system in Colleges and universities, and uses the cross-function to design and build the automatic course scheduling system in Colleges and universities. And select a college’s course scheduling system from this city for research, and use the Genetic-Ant hybrid algorithm to improve the original system to form a new system, called the original system A, and called the improved new system B, to compare the operation time and system suitability of the two systems. The results show that the fitness of system B is better than that of system A. When the scheduling unit is 100, the fitness of system A is 181, and system B is 203. When the scheduling unit is 400, the fitness of system B is 14 higher than that of system A. When the scheduling unit is 800, the fitness of system B is 64 higher than that of system A. Thus, the hybrid algorithm of genetic ant colony can improve the rationality of the curriculum.

Design of the B/S Model-Based Automated Course Scheduling System of Universities

2013 ◽  
Vol 760-762 ◽  
pp. 1773-1776
Author(s):  
Dong Ju Du ◽  
Hai Long Shao
Keyword(s):  
Network Architecture ◽  
Scheduling Algorithm ◽  
Algorithm Analysis ◽  
Teaching Management ◽  
Scheduling System ◽  
Overall Design ◽  
Course Scheduling ◽  
Function Module ◽  
S Model ◽  
Management Work

In order to lift the restrictions of classrooms, teachers, classes and time on the course scheduling, the B/S network architecture and ASP.NET technology are taken as the development tools to introduce the design of the course scheduling system from the perspectives of course scheduling algorithm analysis, overall design and each function module for the purpose of implementing the intelligentized and humanized course scheduling process and perfecting the teaching management work.

Research on the Application of College Automated Course Scheduling System Based on Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1782-1785
Author(s):  
Xiu Ying Li ◽  
Dong Ju Du
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Teaching Quality ◽  
Combinatorial Optimization Problem ◽  
Scheduling System ◽  
Course Scheduling ◽  
Strong Ability

A reasonable curriculum contributes to the improvement of the training and teaching quality of college students. Using computer which is speed and strong ability to arrange curriculum automatically is imperative. Automatically curriculum arrangement is a constrained, multi-objective and intricate combinatorial optimization problem. Based on genetic algorithm of population search, it is suitable to process complex and nonlinear optimization problems which it difficult to solve for traditional search methods. In this paper solves complex automated course scheduling using genetic algorithms.

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