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During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises. [ABSTRACT FROM AUTHOR]

In groundwater problems, there always is some uncertainty associated with appropriate values for aquifer parameters. Therefore an optimal remediation strategy identified by assuming a deterministic description of the system may not yield an optimal and feasible design. This work develops a robust genetic algorithm (GA) approach that takes into account the uncertainty of hydraulic conductivity values when determining the best remediation design possible. Within a generation of the robust GA, all designs are evaluated using the same realization of the heterogeneous hydraulic conductivity field, but the realizations vary between GA generations. Ongoing performance of the designs is measured and is used in the GA evolution process. While the robust GA is a multiple realization method, minimal additional computation effort over that of a basic GA is required to identify robust designs. The robust GA is applied to two cases of varying heterogeneity of an example contaminated aquifer remediated by a pump-and-treat system. The goal of the optimization problem is to identify low cost designs that meet constraints on contaminant concentrations, hydraulic heads, and well extraction rates given uncertainty in hydraulic conductivity values. In addition, the basic GA and noisy GA are applied to the same problem for comparison. The robust GA identified designs with lower costs and using fewer objective function evaluations than the noisy GA designs. However, the noisy GA designs generally have lower concentration constraint violation and higher reliability than the robust GA designs, particularly in the more heterogeneous case. As heterogeneity and uncertainty in hydraulic conductivity increases, remediation costs increase as more wells are required to satisfy the remediation goal and reliability decreases. Nevertheless, both the robust GA and noisy GA identified designs that performed better in constraint feasibility and reliability than the solutions found by the basic GA, which assumed deterministic conditions. Additionally, this work shows that assuming a deterministic description of the aquifer—either homogeneous or heterogeneous—can result in significant underdesign and poor remediation performance. [ABSTRACT FROM AUTHOR]

Presents a study which compared two methods for constraint handling within the genetic algorithm framework. Optimization approaches used in designing ground-water remediation systems; Granular activated carbon treatment design example; Air-stripping tower design example; Numerical experiments; Results and discussion.