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An enhancement of genetic algorithm applied in courier vehicle routing / Jennifer T. Escandor, and Kyke Anthony S. Rosario. 6

By: Escandor, Jennifer T. and Rosario, Kyke Anthony S. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 201846Edition: Description: 28 cm. 253 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: Genetic Algorithm is used in finding optimal or near optimal solutions to difficult problems. The algorithm begins with a set of potential solutions known as the population, which evolves during the process and generates a more optimal set of solutions. The algorithm is applied most commonly in optimization problems, one of which is the vehicle routing problem, which aims for the shortest route that passes only once through a set of points. Other applications of Genetic Algorithm include image processing, circuit design, and scheduling problems. An enhancement of the existing algorithm was proposed to produce a more effective Genetic Algorithm. During the study, the researchers found three problems: the algorithm's failure to produce a value equivalent to the optimal solution, its tendency to output invalid solutions, and re-computation of fitness values. The enhanced algorithm is now able to produce optimal solutions with no invalid solutions present and is able to minimize execution time when running the enhanced algorithm. Experimental results show that the enhanced Genetic Algorithm is better and more effective than the current algorithm. 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 Es3 2018 (Browse shelf)	Available		FT6455

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Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2018. 56

ABSTRACT: Genetic Algorithm is used in finding optimal or near optimal solutions to difficult problems. The algorithm begins with a set of potential solutions known as the population, which evolves during the process and generates a more optimal set of solutions. The algorithm is applied most commonly in optimization problems, one of which is the vehicle routing problem, which aims for the shortest route that passes only once through a set of points. Other applications of Genetic Algorithm include image processing, circuit design, and scheduling problems. An enhancement of the existing algorithm was proposed to produce a more effective Genetic Algorithm. During the study, the researchers found three problems: the algorithm's failure to produce a value equivalent to the optimal solution, its tendency to output invalid solutions, and re-computation of fitness values. The enhanced algorithm is now able to produce optimal solutions with no invalid solutions present and is able to minimize execution time when running the enhanced algorithm. Experimental results show that the enhanced Genetic Algorithm is better and more effective than the current algorithm.

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