Enhancing saptarini, suasnawa, and capitayani's modified distributed genetic algorithm for optimized faculty workload and course assignment - Adrian Angelo D. Abelarde, Joshua D. Bumanlag. 4 0 16; 5 (2024)
ABSTRACT: This thesis presents an enhanced Distributed Genetic Algorithm (DGA) that utilizes the combined capabilities of Differential Evolution (DE), chaotic mapping, and asynchronous communication to transform faculty scheduling systems. The proposed DifferentialGenetic Algorithm (DGA) combines DE's strong mutation and crossover mechanisms with the extensive search capabilities of chaotic mapping. This integration results in a DGA that exhibits exceptional genetic variety, effectively preventing premature convergence. Asynchronous communication improves the algorithm by facilitating a smooth and efficient transmission of genetic information among subpopulations. The combination of these sophisticated methods results in an impressive decrease in an impressive decrease in computational time and a significant improvement in solution accuracy. The study's positive outcomes demonstrate the model's immediate usefulness in academic scheduling and suggest its potential effectiveness in other areas, which justifies further investigation into various optimization scenarios. The research highlights the crucial equilibrium between computing speed and accuracy of solutions and suggests the need for further exploration of this relationship in different computational settings.
Undergraduate Thesis: (Bachelor of Science on Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024.
Keywords: 2 -- 0; 6; 20; 20; 2