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oai:www.ideals.illinois.edu:2142/81637 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

2015-09-25T20:19:38Z 2015-09-25T20:19:38Z 10000-01-01 2004 2004 Goldberg, David E. Chen, Ying-Ping 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. Made available in DSpace on 2015-09-25T20:19:38Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3130894.pdf: 4387284 bytes, checksum: 189d037ff2facfdb442381af36fc54e4 (MD5) Previous issue date: 2004 Embargo set by: Seth Robbins for item 82918 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 147 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004. http://hdl.handle.net/2142/81637 (MiAaPQ)AAI3130894 eng Computer Science Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.