Machine learning approach for predicting maize crop yields using multiple linear regression and backward elimination

The University teaching timetabling problem is a combinatorial optimization problem in which a set of events has to be scheduled in time slots and located in suitable rooms. This problem is in class NP-hard problems that are very difficult to solve with the classical algorithms. Several studies exist to resolve the many conflicting constraints that exist in a timetable schedule using computational intelligent approaches. These computational intelligent approaches simply search the domain space for a goal state that satisfies the problem constraints. Among these techniques, evolutionary algorithms proved to perform well to optimize the extreme timetabling constraints. A weighted sum formula was used to prioritize lecturers according to their availabilities handles conflicts. The algorithm incorporates several operators (selection, crossover, mutation) to enhance search efficiency and also ensure that the best chromosomes that are selected. The genetic algorithm was tested and compared with a set of other algorithms from the literature. The experimental results showed that the genetic algorithm was able to produce feasible results for the university teaching timetable. Future research can focus on applying iterative search techniques to find the best optimal solutions with regards to lecturers’ and students’ availability especially for students repeating courses from the previous semesters.