Multi Population Hybrid Genetic Algorithms for University Course Timetabling

Abstract

University course timetabling is one of the important and time consuming issues that each University is involved with at the beginning of each university year. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or almost optimal feasible solutions instead of exact solutions. Genetic algorithms, because of their multidirectional search property, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP) are proposed: FGARI, FGASA and FGATS. In the proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainty and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.