Interactive genetic algorithm framework for long term groundwater monitoring design

In standard optimization approaches for water resources management problems, the designer is responsible for correctly formulating mathematical equations to describe the system objectives and constraints. The search for optimal or near-optimal solutions is made under the assumption that these formulated objectives and constraints completely describe the system. However, in real systems that is often not true. Many qualitative criteria can be integral parts of the design analysis that numerically based algorithms cannot capture. For such problems, designer interaction with the search algorithm can help the search be more creative and inclusive. Genetic algorithms are ideally suited for incorporating such interaction in their usual search process, and can successfully evolve solutions that are optimal with respect to both qualitative and quantitative objectives. Under an interactive approach, the genetic algorithm performs the usual operations of selection, crossover, and mutation, but the user evaluates the suitability ('fitness') of candidate solutions, enabling objectives that cannot be quantified to be included in the search process. In multi-objective problems, where quantitative objectives can be as important as qualitative fitness of designs, analysis of designs is done based on tradeoff fronts made from both quantitative and qualitative information. In this paper, we demonstrate the use of interactive genetic algorithms for long term groundwater monitoring problems, which have multiple numerical and subjective objectives. We also analyze the effects on the optimal monitoring designs of using an interactive optimization approach instead of more traditional numerical optimization approaches.