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Title:Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice
Author(s):Chen, Ying-Ping
Doctoral Committee Chair(s):Goldberg, David E.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
Abstract:The study finds that using promoters on the chromosome can improve nucleation potential and promote correct building-block formation. It also observes that the linkage learning genetic algorithm has a consistent, sequential behavior instead of different behaviors on different problems as was previously believed. Moreover, the competition among building blocks of equal salience is the main cause of the exponential growth of convergence time. Finally, adopting subchromosome representations can reduce the competition among building blocks, and therefore, scalable genetic linkage learning for a unimetric approach is possible.
Issue Date:2004
Type:Text
Language:English
Description:147 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.
URI:http://hdl.handle.net/2142/81637
Other Identifier(s):(MiAaPQ)AAI3130894
Date Available in IDEALS:2015-09-25
Date Deposited:2004


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