Hybrid technologies and genetic algorithms applied to school lecture timetable generation

ABSTRACT

This paper proposes an advanced lecture timetabling system utilizing hybrid technologies and genetic algorithms for optimal scheduling whereby resource allocation challenges and conflicts are addressed and minimized. Genetic algorithms offer a solution to complex scheduling problems by mimicking natural selection principles, thereby refining lecture timetables through selection, crossover, and mutation. The system formulates the timetabling problem as a genetic algorithm optimization task by integrating constraints into the fitness function, which include resource availability, activity dependencies, and user preferences; facilitating evaluation based on predefined objectives like conflict minimization and resource utilization maximization. The genetic algorithm employs a population-based search strategy, iteratively refining timetables over generations. The system demonstrates high solution quality, efficiency, and scalability when compared with traditional methods, thereby providing benefits to educational institutions and the like.

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WSN 189 (2024) 288-310

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