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Title:Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms
Author(s):Yan, Shengquan
Doctoral Committee Chair(s):Barbara Minsker
Department / Program:Civl and Environmental Engineering
Discipline:Civl and Environmental Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Environmental Sciences
Abstract:Real-world optimization problems are often inherently uncertain. The last focus of the research is to extend the adaptive modeling technique in a stochastic optimization framework so that robust optimal solutions can be efficiently identified in the presence of parameter uncertainty. The developed algorithm, called Noisy-AMGA, minimizes the expected fitness function with a constrained reliability level. As in AMGA, the meta-models in Noisy-AMGA are online updated but they are trained to predict the expected outputs. The method was applied to two remediation case studies, where the primary source of uncertainty stems from hydraulic conductivity values in the aquifers. The results show that the technique can lead to far more reliable solutions with significantly less computational effort.
Issue Date:2006
Description:171 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Other Identifier(s):(MiAaPQ)AAI3243030
Date Available in IDEALS:2015-09-25
Date Deposited:2006

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