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This study examines the written and visual results of a participatory systems-mapping process used to explore undergraduate research at a large, public research university in the United States. With the university's transition to a high-impact practice model, the institutional value of undergraduate research has increased, but challenges remain in implementing the practice equitably and inclusively, especially in the complex environment of higher education. The systems-mapping process reveals the subtle, often conflicting dynamics that underlie the undergraduate research enterprise, while simultaneously supporting the emergence of a shared vision, or story, of what the undergraduate research experience could be.

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. DOI: 10.1061/(ASCE)WR.1943-5452.0000053 CE Database subject headings: Algorithms; Computation; Water resources; Water management; Planning. Author keywords: Evolutionary algorithm; Evolutionary computation; Genetic algorithm.

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.