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Enhancing saptarini, suasnawa, and capitayani's modified distributed genetic algorithm for optimized faculty workload and course assignment / Adrian Angelo D. Abelarde, Joshua D. Bumanlag. 6

By: Adrian Angelo D. Abelarde, Joshua D. Bumanlag. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; April 2024.46Edition: Description: 28 cm. ix, 102 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: 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. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	QA76.9.A43 A24 2024 (Browse shelf)	Available		FT7857

Total holds: 0

Undergraduate Thesis: (Bachelor of Science on Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024. 56

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.

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