VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Knowledge retention with genetic algorithms by multiple levels of representation

Simple item page
dc.contributor.authorDing, Yingjiaen
dc.contributor.committeechairNutter, Jane Terryen
dc.contributor.committeememberFox, Edward A.en
dc.contributor.committeememberLee, John A. N.en
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2014-03-14T21:51:02Zen
dc.date.adate2009-12-05en
dc.date.available2014-03-14T21:51:02Zen
dc.date.issued1991-05-15en
dc.date.rdate2009-12-05en
dc.date.sdate2009-12-05en
dc.description.abstractLow-level representations have proven to be good at certain kinds of adaptive learning. High-level representations make effective use of existing knowledge and perform inference well. To promote using both forms of representation cooperatively rather than engaging in the perennial sectarian debate of supporting one paradigm at the expense of the other, this thesis presents a prototype system demonstrating knowledge retention using genetic algorithms and multiple levels of representation and learning. The prototype uses a mid-level of representation and transformations upward and downward for retaining domain-specific knowledge to bridge the gap between the high-level representation and learning and the genetic algorithm level. The thesis begins with an overview of the work, briefly introduces the principles of genetic algorithms, and states an illustrative domain. Then it reviews related work and two supportive systems. After that, it gives a general description of the prototype system's structure, three levels of representation, two transformations, and three levels of learning. Next, it describes methods of implementing the prototype system in some detail. Finally, it shows results with discussion, and points out conclusions and future work.en
dc.description.degreeMaster of Scienceen
dc.format.extentv, 95 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-12052009-020026en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12052009-020026/en
dc.identifier.urihttp://hdl.handle.net/10919/46125en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1991.D564.pdfen
dc.relation.isformatofOCLC# 24346681en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1991.D564en
dc.subject.lcshKnowledge representation (Information theory) -- Researchen
dc.titleKnowledge retention with genetic algorithms by multiple levels of representationen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LD5655.V855_1991.D564.pdf
Size:
5.06 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Masters Theses