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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2001: Evolutionary Multi-Criterion Optimization pp 681–695Cite as

The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1993)

Abstract

We present an evolutionary approach to a difficult, multiobjective problem in groundwater quality management: how to pump-and-treat (PAT) contaminated groundwater to remove the most contaminant at the least cost. Although evolutionary multiobjective (EMO) techniques have been applied successfully to monitoring of groundwater quality and to containment of contaminated groundwater, our work is a first attempt to apply EMO to the long-term (ten year) remediation of contaminated water. We apply an improved version of the Niched Pareto GA (NPGA 2) to determine the pumping rates for up to fifteen fixed-location wells. The NPGA2 uses Pareto-rank-based tournament selection and criteria-space niching to find nondominated frontiers. With 15 well locations, the niched Pareto genetic algorithm is demonstrated to outperform both a single objective genetic algorithm (SGA) and enumerated random search (ERS) by generating a better tradeoff curve.

Keywords

  • Pareto Optimal Solution
  • Granular Activate Carbon
  • Tournament Selection
  • Tradeoff Curve
  • Nondominated Sorting

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Department of Geological Engineering and Sciences, Michigan Technological University, Houghton, Michigan, USA

    Mark Erickson & Alex Mayer

  2. Department of Mathematics and Computer Science, Northern Michigan University, Marquette, Michigan, USA

    Jeffrey Horn

Authors
  1. Mark Erickson
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  2. Alex Mayer
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  3. Jeffrey Horn
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler & Lothar Thiele & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Erickson, M., Mayer, A., Horn, J. (2001). The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_48

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  • DOI: https://doi.org/10.1007/3-540-44719-9_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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