The genetic algorithm (GA) is used as an optimization tool to estimate
water quality model parameters in a calibration scenario. The GA is f
ound to be a useful calibration tool, capable of providing least-squar
es parameter estimates while incorporating field observations as const
raints and accumulating useful information about the response surface.
Because the GA provides a directed, randomized search using a populat
ion of points, a database of information about the response surface, p
arameter correlation, and objective function sensitivity to model para
meters is obtained. Synthetic data with and without error are used ini
tially to investigate the potential of the GA for model calibration ap
plications. A case study is then carried out to confirm GA performance
with field data. Constraints are included successfully in the GA sear
ch using either a penalty function or a special decoding operation. Ho
wever, results show that the GA with the penalty function outperforms
the GA with the decoder. Furthermore, parameter estimation is found to
be improved by the inclusion of multiple-response data. For ill-posed
problems, the GA provides several parameter estimates, all performing
equally well mathematically.