{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-2541"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-2541","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Genetic algorithms for the traveling salesman problem using edge assembly crossovers","abstract":"The central issue in creating new genetic algorithms is the algorithm's crossover method. My focus is on a particular crossover known as the Edge Assembly Crossover, or EAX, by Nagata and Kobayashi. The basics of a what make up a genetic algorithm is reviewed. The traveling salesman problem is defined. The EAX as an algorithm within an algorithm is explained. The crossover's implementation is original and is listed. The use of the graphic user interface, TSP View, used to run algorithms is explained as well as the extensions to the interface that were implemented for this study. The results of running a genetic algorithm using the EAX against traveling salesman problems, with a focus on ATT532, is discussed and compared to runs using other optimization algorithms. The question of why EAX works is addressed with conjectures for a possible future research path.","abstract_html":"The central issue in creating new genetic algorithms is the algorithm's crossover method. My focus is on a particular crossover known as the Edge Assembly Crossover, or EAX, by Nagata and Kobayashi. The basics of a what make up a genetic algorithm is reviewed. The traveling salesman problem is defined. The EAX as an algorithm within an algorithm is explained. The crossover's implementation is original and is listed. The use of the graphic user interface, TSP View, used to run algorithms is explained as well as the extensions to the interface that were implemented for this study. The results of running a genetic algorithm using the EAX against traveling salesman problems, with a focus on ATT532, is discussed and compared to runs using other optimization algorithms. The question of why EAX works is addressed with conjectures for a possible future research path.","abstract_has_math":false,"creators":["Seppala, Dwain Alan"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wolfgang Bein"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-01-01T08:00:00Z","date_published":"2003-01-01T08:00:00Z","updated_at":"2026-07-24T05:25:40Z","subjects":[],"languages":["English"],"rights":["IN COPYRIGHT. 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The use of the graphic user interface, TSP View, used to run algorithms is explained as well as the extensions to the interface that were implemented for this study. The results of running a genetic algorithm using the EAX against traveling salesman problems, with a focus on ATT532, is discussed and compared to runs using other optimization algorithms. The question of why EAX works is addressed with conjectures for a possible future research path."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Genetic algorithms for the traveling salesman problem using edge assembly crossovers"]}]}],"canonical_facts":{"dc:contributor":["Wolfgang Bein"],"dc:creator":["Seppala, Dwain Alan"],"dc:description.abstract":["The central issue in creating new genetic algorithms is the algorithm's crossover method. My focus is on a particular crossover known as the Edge Assembly Crossover, or EAX, by Nagata and Kobayashi. The basics of a what make up a genetic algorithm is reviewed. The traveling salesman problem is defined. The EAX as an algorithm within an algorithm is explained. The crossover's implementation is original and is listed. The use of the graphic user interface, TSP View, used to run algorithms is explained as well as the extensions to the interface that were implemented for this study. The results of running a genetic algorithm using the EAX against traveling salesman problems, with a focus on ATT532, is discussed and compared to runs using other optimization algorithms. The question of why EAX works is addressed with conjectures for a possible future research path."],"dc:format":["pdf"],"dc:identifier":["10.25669/sm98-fs32","https://oasis.library.unlv.edu/rtds/1542","https://oasis.library.unlv.edu/context/rtds/article/2541/viewcontent/uc.pdf"],"dc:language":["English"],"dc:publisher":["University of Nevada, Las Vegas"],"dc:rights":["IN COPYRIGHT. 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