<![CDATA[Class scheduling is a crucial aspect of higher education academic management, facing various constraints, such as classroom availability, lecturer teaching hours, classroom capacity, and student needs to ensure they attend courses without scheduling conflicts. Manual schedule creation is often time-consuming and prone to errors, especially as the number of courses, lecturers, and classrooms increases. Therefore, an optimization method capable of producing effective and efficient schedules is required. This study applies the Memetic Algorithm (MA) to solve the class schedule optimization problem. The Memetic Algorithm is a development of the Genetic Algorithm that combines population evolution with local search to improve solution quality. In this study, each solution is represented as a set of class schedules that must satisfy various hard and soft constraints. The optimization process involves population initialization, selection, crossover, mutation, and solution refinement using local search. The expected outcome of this research is the creation of a scheduling system capable of producing an optimal lecture schedule with minimal conflict, more effective space utilization, and efficient computing time. The application of the Memetic Algorithm is expected to be an alternative solution for managing academic scheduling in higher education.]]>
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