| In most real-world groundwater monitoring optimization applications, a number of important subjective issues exist. Most of these issues are difficult to rigorously represent in numerical optimization procedures. Popular norms, such as objectives and constraints, implemented within current optimization methods make many simplifying assumptions about the true complexity of the problem. As a result, such norms can fall short of characterizing all the relevant information related to the problem, which the expert (engineers, stakeholders, regulators, etc) might be aware of. Hence, there is a need for optimization and decision-aiding approaches that include subjective criteria within the search process for promising solutions.; 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. |
