Knowledge retention with genetic algorithms by multiple levels of representation

Low-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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

1991

Author dc:creator

• Ding, Yingjia

Chair dc:contributor.committeechair

• Nutter, Jane Terry

Committee members dc:contributor.committeemember

• Fox, Edward A.
• Lee, John A. N.

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

etd-12052009-020026

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/46125