Abstract
The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.
Highlights
With the rapid development of economic and cultural level, the scale of university enrollment continues to expand, and the professional setting and course setting is developing in depth and breadth
Many traditional methods for solving this problem show low-efficiencies in several aspects, such as a high conflict rate of teacher resources or classroom resources, low degree of satisfaction among students or teachers and so on. These methods do not meet the requirements of modern university educational administration management
DESIGN OF COARSE -GRAINED PARALLEL GENETIC ALGORITHM WITH HADOOP
Summary
With the rapid development of economic and cultural level, the scale of university enrollment continues to expand, and the professional setting and course setting is developing in depth and breadth. Many traditional methods for solving this problem show low-efficiencies in several aspects, such as a high conflict rate of teacher resources or classroom resources, low degree of satisfaction among students or teachers and so on. These methods do not meet the requirements of modern university educational administration management. In this paper, based on the cloud computing platform with Hadoop, an improved method fusing Coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is presented to solve the university course-timetabling problem.
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