A Self -Adaptive Hybrid Genetic Algorithm for Optimal Groundwater Remediation Design
The application of the e-SAHGA algorithm to a hypothetical groundwater remediation design problem showed 90% reliability in identifying the solution faster than the SGA, with average savings of 64% across 100 runs with different random initial populations. Finally, e-SAHGA was tested on a field-scale remediation design problem, re-evaluation of the remediation system for Umatilla Army Depot, where it gave computational savings between 30% and 60% and, for one solution method, found a solution that was 4% better than the one found by the SGA.
University of Illinois - Urbana-Champaign
Networked Digital Library of Theses and Dissertations
University of Illinois at Urbana-Champaign