Repair and Initialization Functions in the Generation of School Schedules with Genetic Algorithms

The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1144Dimensions.Open Alex.