A Hybrid Evolutionary Approach to Solve University Course Allocation Problem
This addresses the practical problem of manually scheduling university courses for faculty, though it appears incremental as it builds on existing evolutionary methods.
The paper tackles the university course allocation problem by developing a hybrid evolutionary algorithm combining Local Repair Algorithm and Modified Genetic Algorithm to generate optimized course assignments that fulfill constraints for clash-free schedules. The proposed algorithm showed better efficiency in accuracy and time compared to baseline optimization algorithms.
This paper discusses various types of constraints, difficulties and solutions to overcome the challenges regarding university course allocation problem. A hybrid evolutionary algorithm has been defined combining Local Repair Algorithm and Modified Genetic Algorithm to generate the best course assignment. After analyzing the collected dataset, all the necessary constraints were formulated. These constraints manage to cover the aspects needed to be kept in mind while preparing clash free and efficient class schedules for every faculty member. The goal is to generate an optimized solution which will fulfill those constraints while maintaining time efficiency and also reduce the workload of handling this task manually. The proposed algorithm was compared with some base level optimization algorithms to show the better efficiency in terms of accuracy and time.