Applying Evolution Strategies to a University Timetabling System

Determining the best, or near best timetable of lecture/courses for a university department, which optimizes enrollment, is a challenging problem. Described herein is a platform developed to accept student-generated data of course preferences, the university department curriculum, and resource constraints. The platform then generates the best or near best feasible schedule using an Evolutionary Computation Algorithm combining crossover and mutation for a fixed population. In addition, the platform permits manipulation of the population size, the initial mutation rate, the rule for varying the mutation rate, and the crossover point the platform order to examine the impact of these parameters and to determine the best values for this class of timetable problem. While this is an ongoing project, included is a sample run on an artificial data sample which shows the efficiency of the algorithm.