This paper builds on the work of Meyer and Brill (1988) and subsequent work by Meyer et al. (1990, 1992) on the optimal location of a network of groundwater monitoring wells under conditions of uncertainty. We investigate a method of optimization using genetic algorithms (GAs) which allows us to consider the two objectives of Meyer et al. (1992), maximizing reliability and minimizing contaminated area at the time of first detection, separately yet simultaneously. The GA‐based solution method has the advantage of being able to generate both convex and nonconvex points of the trade‐off curve, accommodate nonlinearities in the two objective functions, and not be restricted by the peculiarities of a weighted objective function. Furthermore, GAs have the ability to generate large portions of the trade‐off curve in a single iteration and may be more efficient than methods that generate only a single point at a time. Four different codings of genetic algorithms are investigated, and their performance in generating the multiobjective trade‐off curve is evaluated for the groundwater monitoring problem using an example data set. The GA formulations are compared with each other and also with simulated annealing on both performance and computational intensity. Simulated annealing relies on a weighted objective function which can find only a single point along the trade‐off curve for each iteration, while all of the multiple‐objective GA formulations are able to find a larger number of convex and nonconvex points of trade‐off curve in a single iteration. Each iteration of simulated annealing is approximately five times faster than an iteration of the genetic algorithm, but several simulated annealing iterations are required to generate a trade‐off curve. GAs are able to find a larger number of nondominated points on the trade‐off curve, while simulated annealing finds fewer points but with a wider range of objective function values. None of the GA formulations demonstrated the ability to generate the entire trade‐off curve in a single iteration. Through manipulation of GA parameters certain sections of the trade‐off curve can be targeted for better performance, but as performance improves at one section it suffers at another. Run times for all GA formulations were similar in magnitude.

Starting in 1985, comprehensive Remedial Action Plans were initiated to restore impaired beneficial uses in Great Lakes Areas of Concern. These plans were a catalyst for developing programs to remediate contaminated sediments. In 1987, the U.S. Environmental Protection Agency implemented the Assessment and Remediation of Contaminated Sediment Program to: measure contaminant concentrations in sediments and their potential effects on aquatic life; evaluate risks to wildlife and human health; and test technologies that might be used to clean up these contaminated sediments. In 2002, the U.S. Great Lakes Legacy Act was signed into law with the intent to remediate contaminated sediments at Great Lakes Areas of Concern. Before Great Lakes Legacy Act, only limited progress had been made in addressing contaminated sediments, a major, intractable issue impacting 9 of the 14 listed beneficial use impairments in Areas of Concern. Between 2004 and 2017, Great Lakes stakeholders have completed a total of 46 contaminated sediment remediation projects in U.S. Areas of Concern, resulting in the remediation of over 6.6 million m3 of contaminated sediments at a cost of over $1 billion. Although much has been accomplished, more contaminated sediment remediation must be undertaken to fully restore Areas of Concern. The Great Lakes Legacy Act and Great Lakes Restoration Initiative have been essential components for completing this important remediation and restoration work in Areas of Concern that is resulting in significant economic and environmental benefits.

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