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dc.contributor.advisorShing, Man-Tak
dc.contributor.authorParker, Gary B.
dc.dateSeptember 1992
dc.date.accessioned2012-11-29T16:18:21Z
dc.date.available2012-11-29T16:18:21Z
dc.date.issued1992-09
dc.identifier.urihttp://hdl.handle.net/10945/23911
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractSearch of an unknown space by a physical agent (such as an autonomous vehicle) is unique in search as the customarily most important goal (to reduce the computation time required to obtain the shortest distance) is not as important as minimal movement. there is a real-time aspect since the agent is actually moving; using energy every step of the way. Having limited energy resources and knowledge of the terrain (only adjacent nodes), the key factor for the physical agent's search algorithm is reduction of steps. Hence, any heuristic that can help keep step count to a minimum must be considered. Korf and Shing addressed this in separate works. Both made use of known information about the frontier node's distance from the current node in addition to a heuristic estimating the distance from goal. In this thesis, we present a simple genetics-based method to produce adaptive, efficient multi-heuristic search strategies for the real-time problem. Extensive empirical study shows that this approach produced search strategies with much better performance over existing search algorithms for most terrain types. The methodologies used to develop these improved strategies for our specific case, are also applicable to a multitude of real-time search/optimization problems in the general case.en_US
dc.description.urihttp://archive.org/details/geneticalgorithm00park
dc.format.extent124 p.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleGenetic algorithms for the development of real-time multi-heuristic search strategiesen_US
dc.typeThesisen_US
dc.contributor.secondreaderLee, Yuh-Jeng
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentDepartment of Computer Science
dc.subject.authorArtificial intelligenceen_US
dc.subject.authorGenetic algorithmsen_US
dc.subject.authorSearchen_US
dc.subject.authorPath planningen_US
dc.description.serviceLieutenant Commander, United States Navyen_US
etd.thesisdegree.nameM.S. in Computer Scienceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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