Fitness values are then used by the GA to determine which individual sampling designs are allowed to reproduce and evolve in later generations in a process analogous to Darwinian evolution. The management model undergoes several iterations of evolving new populations of potential sampling plans until the GA converges to several optimal or near-optimal sampling designs. Currently, uncertainty is being incorporated into the management model within a multi-objective framework. This effort is expected to quantify the Pareto frontier, which represents the tradeoff between cost and uncertainty for sites undergoing long-term monitoring. The model was applied using published data from the Hill Air Force Base site, which is currently implementing monitored natural attenuation.
Results show that significant potential reducing monitoring costs.
Reed, P., B. S. Minsker, and A. J. Valocchi. Cost effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resources Research. Reed, P. B. S. Minsker, and D. E. Goldberg. Designing a competent simple genetic algorithm for search and optimization.