Performance Comparison of Evolutionary Algorithms for University Course Timetabling Problem

Abstract

In literature, University Course Timetabling Problem (UCTP) is a well known combinational problem. The main reasons to study this problem are the intrinsic importance at the interior of universities, the exponential number of solutions, and the distinct types of approaches to solve this problem. Due to the exponential number of solutions (combinations), this problem is categorized as NP-hard. Generally, Evolutionary Algorithms (EA) are efficient tools to solve this problem. Differential Evolution (DE) has been widely used to solve complex optimization problems on the continuous domain, Genetic Algorithms (GA) has been adopted to solve different types of problems and even as point of comparison between algorithms performance. This paper examines and compares the performance depicted by two approaches based on EA to solve the UCTP: the DE and the GA approaches. The experiments use a set of 3 real life UCTP instances, each instance contains different characteristics and are based on Mexican universities. In the experiments, we used the optimal input parameters for the solvers, and we performeda qualitative-quantitative comparison between the final solutions. The results showed the best performance for the solution based on the DE algorithm. This work canbe easily extended to use other algorithms and UCTP instances.