Constraint satisfaction algorithms: edition of timetables in the license-master-doctorate system - Maurice Comlan; Corentin Allohoumbo (Computer Science and Information Technologies, 2023)
In this paper, we studied some algorithms for solving constraint satisfaction problem (CSP) and then applied them to solve the problem of generating schedules in a university setting. In other words, we studied the genetic algorithm, the simulated annealing, the hill climbing, a hybridization of the genetic algorithm and the simulated annealing as well as a hybridization of the genetic algorithm and the hill climbing. These algorithms have been tested on the problem of scheduling in a university environment. The hybrid uses hill climbing or simulated annealing to improve each individual in the starting population to a certain stopping point. These individuals are then sent to the genetic algorithm. Our results show that the hybridization of the genetic algorithm with a metaheuristic gives better execution time and performs better as the problem size increases compared to the classical genetic algorithm.