On the Fitness Measure of Genetic Algorithm for Generating Institutional Lecture Timetable

We conducted a study on the performance of genetic algorithm in designing institutional lecture time table, using empirical
data of a college in a University. The study was focused on assessing the effectiveness of the algorithm given a number of
hard constraints and a limited number of resources. The algorithm was implemented in C++. Several tests representing
different scenarios were run and we found that genetic algorithm would always search for an optimum lecture allocation
that satisfies the hard constraints in generating Institutional lecture time table. The algorithm can only reach the fitness of 1
when all the hard constraints have been satisfied. The fitness of the algorithm can be improved upon by the provision of
adequate number and sufficient capacity of resources to carter for the hard constraints. We therefore, conclude that genetic
algorithm works best in an environment where resources capacity and availability do not constitute some bottleneck.