An Optimized Machine Learning Model for Automating Academic Scheduling

Abstract

Timetable development is a complex and lengthy task that might require lots of effort and attention to the details. Timetables can be useful in many ways such as planning academic programs in schools & colleges, establishing study timetables and transport timetables. Conventional procedures to develop timetables take much manual work and do not work without errors. The problem of incompatibility is overcome in this paper by employing a Genetic Algorithm (GA) in timetable design. With the application of GA, it is possible to make accurate and efficient schedules and to make minimum human errors and save time which is taken in the process.