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Bergman, Lawrence A. Voulgaris, Petros G. Johnson, Erik Arthur 2015-09-25T22:34:30Z 2015-09-25T22:34:30Z 10000-01-01 1997 1997 "Ideas for improving the efficiency of Monte Carlo simulation (MCS) is the subject of the final section. Determining the low failure probabilities of typical engineering systems is quite difficult without using millions of MCS realizations to characterize the probability distribution. Several links between some MCS variance reduction techniques and Genetic Algorithms are discussed. A simple example, incorporating Genetic Algorithm operators into MCS, is shown to estimate probabilities a couple orders of magnitude smaller than standard MCS. Several concepts for characterizing the sense of a realization's ""importance"", such as discrepancy sensitivity and phase space velocity, are examined and found to successfully quantify realization importance." Made available in DSpace on 2015-09-25T22:34:30Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9812643.pdf: 14905415 bytes, checksum: 3ad5cea61254cd9a35380e78ec975f52 (MD5) Previous issue date: 1997 Embargo set by: Seth Robbins for item 86406 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 368 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997. http://hdl.handle.net/2142/85125 (MiAaPQ)AAI9812643 eng Applied Mechanics On-Line Monitoring, Control, and Reliability of Structural Dynamical Systems text Aerospace Engineering Aerospace Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.