Genetic Algorithm Class Scheduling System

This genetic algorithm-based class scheduling code has been optimized through debugging to achieve efficient and accurate timetable generation. The scheduling system architecture can incorporate student course selection, teacher and classroom resource management, and time conflict resolution functionalities. The implementation utilizes key genetic algorithm operations including chromosome encoding (representing class schedules as individuals), fitness functions evaluating timetable quality, tournament selection, single-point crossover, and mutation operators for solution space exploration. To transform this code into a complete system, you'll need to independently develop project files encompassing user interface components, database integration for persistent storage, and system administration modules. The provided code constitutes the core scheduling engine, which can be extended and modified according to specific requirements. This genetic algorithm approach enables adaptive optimization by evolving populations of timetable solutions over generations, balancing multiple constraints like teacher availability, room capacity, and curriculum requirements. We hope this genetic algorithm-based scheduling system assists in effectively managing course arrangements and resource utilization through its evolutionary optimization capabilities. The algorithm continuously improves solutions by selecting the fittest schedules, combining their features through crossover, and introducing diversity via mutation operations.