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oai:www.ideals.illinois.edu:2142/81884 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Goldberg, David E. Miller, Brad L. 2015-09-25T20:20:51Z 2015-09-25T20:20:51Z 10000-01-01 1997 1997 As genetic algorithms (GA) move into industry, a thorough understanding of how GAs are affected by noise is becoming increasingly important. Noise affects a GA's population sizing requirements, performance characteristics, and computational requirements. This research develops quantitative models for determining the effects of noise on the operation of a GA. Furthermore, the question of how to best optimize the performance of a GA in a noisy environments is investigated. Sampling fitness functions are explored, and techniques for determining the optimal sample size that maximizes the performance of a GA within a fixed computational time bound are presented. Made available in DSpace on 2015-09-25T20:20:51Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9737200.pdf: 4007637 bytes, checksum: 358ba13e45362009fc3ec2d5ba475ff2 (MD5) Previous issue date: 1997 Embargo set by: Seth Robbins for item 83165 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 157 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997. http://hdl.handle.net/2142/81884 (MiAaPQ)AAI9737200 eng Computer Science Noise, Sampling, and Efficient Genetic Algorithms text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.