Adaptive hybrid genetic algorithm for groundwater remediation design

Felipe P. Espinoza, Barbara S. Minsker, David E. Goldberg

Research output: Contribution to journalArticlepeer-review


Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self-adaptive HGA (SAHGA) and compares its performance to a nonadaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible.

Original languageEnglish (US)
Pages (from-to)14-24
Number of pages11
JournalJournal of Water Resources Planning and Management
Issue number1
StatePublished - Jan 2005


  • Evolutionary computation
  • Ground-water management
  • Hybrid methods
  • Numerical models
  • Optimization
  • Remedial action

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Water Science and Technology
  • Management, Monitoring, Policy and Law


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