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dc.contributor.authorRice, Michael Norris
dc.date.accessioned2014-03-03T23:15:35Z
dc.date.available2014-03-03T23:15:35Z
dc.date.issued2005-05
dc.identifier.otherrice_michael_n_200505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/rice_michael_n_200505_ms
dc.identifier.urihttp://hdl.handle.net/10724/22489
dc.description.abstractThis project explores the application of two developing algorithmic paradigms from the field of computational intelligence towards optimized vehicle routing applications within geographic information systems (GIS). Ant Colony Optimization (ACO) is a type of multi-agent, swarm-based algorithm designed to mimic the emergent route-finding behavior of real ants within a colony. Genetic Algorithms (GA) are another nature-inspired type of algorithm designed for evolving optimal or near-optimal solutions to a problem through the use of techniques based on natural selection, crossover, and mutation. The goal of this project is to demonstrate the effectiveness of a newly proposed, hybrid version of these two algorithms, aimed at evolving agents (ants) for optimized routing within swarm-based vehicle routing programs.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectCOMPUTATIONAL INTELLIGENCE
dc.subjectVEHICLE ROUTING
dc.subjectGENETIC ALGORITHMS
dc.subjectANT COLONY OPTIMIZATION
dc.subjectGEOGRAPHIC INFORMATION SYSTEMS
dc.titleA new hybrid computational intelligence algorithm for optimized vehicle routing applications in geographic information systems
dc.typeThesis
dc.description.degreeMS
dc.description.departmentGeography
dc.description.majorGeography
dc.description.advisorE. Lynn Usery
dc.description.committeeE. Lynn Usery
dc.description.committeeXiaobai Yao
dc.description.committeeWalter D. Potter


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