*Interactive Genetic Algorithms for Adaptive Decision Making in Groundwater Monitoring Design
Babbar, Meghna
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https://hdl.handle.net/2142/83297
Description
Title
*Interactive Genetic Algorithms for Adaptive Decision Making in Groundwater Monitoring Design
Author(s)
Babbar, Meghna
Issue Date
2006
Doctoral Committee Chair(s)
Minsker, Barbara S.
Department of Study
Civl and Environmental Engineering
Discipline
Civl and Environmental Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Environmental
Language
eng
Abstract
This research tries to fill this need by proposing and analyzing optimization methodologies, which include subjective criteria of a decision maker (DM) within the search process through continual online interaction with the DM. The design of the interactive systems are based on the Genetic Algorithm optimization technique, and the effect of various human factors, such as human fatigue, nonstationarity in preferences, and the cognitive learning process of the human decision maker, have also been addressed while constructing the proposed systems. The result of this research is a highly adaptive and enhanced interactive framework---Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII)---that learns from the decision maker's feedback and explores multiple robust designs that meet her/his criteria. For example, application of IGAMII on BP's groundwater long-term monitoring case study in Michigan assisted the expert DM in finding 39 above-average designs from the expert's perspective. In comparison, Case Based Micro Interactive Genetic Algorithm (CBMIGA) and Standard Interactive Genetic Algorithm (SIGA) found only 18 and 6 above-average designs, respectively. Moreover, IGAMII used only 75% of the human effort required for CBMIGA and SIGA. IGAMII was also able to monitor the learning process of different human DMs (novices and experts) during the interaction process and create simulated DMs that mimicked the individual human DM's preferences. The human DM and simulated DM were then used together within the collaborative search process, which rigorously explored the decision space for solutions that satisfy the human DM's subjective criteria.
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