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Enhancement of genetic algorithm by J. Zhang applied to tour planning. 6

By: Isabella Mae R. Malonzo, Tracy Louise R. Patacsil. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4538346Edition: Description: Content 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: Following widespread lockdowns, there has been a notable increase in people's desire to travel, leading to longer and more frequent trips. This trend has created a demand for customized itineraries and tour planning. Unfortunately, manual tour planning can be challenging to optimiza, time-consuming, and increasingly complex as the number of locations increases. To automate and improve tour planning, optimization methods can be used, as they leverage algorithms to find efficient routes. The genetic Algorithm (GA), an algorithm that mimics the course of natural evolution, is adept at navigating complex search spaces and finding optimal solutions, making it suitable for solving tour planning challenges. Building upon the work of J. Zhang (2021), this study aims to improve the performance of GA by enhancing the diversity of the population, removing redundant nodes, and reducing the execution time. Two simulators were created, one for each algorithm, to test their performance. The researchers conducted tests on both the existing and enhanced algorithms. This involved the utilization of several test data that contains coordinates of several cities in the Philippines. Based on the results, the enhanced algorithm showed better results compared to the existing algorithm. In conclusion, the enhanced algorithm performed better than the existing 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.A43 .M35 2024 (Browse shelf)	Available		FT7863

Total holds: 0

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

ABSTRACT: Following widespread lockdowns, there has been a notable increase in people's desire to travel, leading to longer and more frequent trips. This trend has created a demand for customized itineraries and tour planning. Unfortunately, manual tour planning can be challenging to optimiza, time-consuming, and increasingly complex as the number of locations increases. To automate and improve tour planning, optimization methods can be used, as they leverage algorithms to find efficient routes. The genetic Algorithm (GA), an algorithm that mimics the course of natural evolution, is adept at navigating complex search spaces and finding optimal solutions, making it suitable for solving tour planning challenges. Building upon the work of J. Zhang (2021), this study aims to improve the performance of GA by enhancing the diversity of the population, removing redundant nodes, and reducing the execution time. Two simulators were created, one for each algorithm, to test their performance. The researchers conducted tests on both the existing and enhanced algorithms. This involved the utilization of several test data that contains coordinates of several cities in the Philippines. Based on the results, the enhanced algorithm showed better results compared to the existing algorithm. In conclusion, the enhanced algorithm performed better than the existing algorithm.

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