A genetic algorithm (GA) is a robust search paradigm based on the principles of natural evolution and biological reproduction (Goldberg, 1989). For optimizing calibration of a water distribution model, a genetic algorithm program first generates a population of trial solutions of the model parameters. A hydraulic solver then simulates each trial solution. The resulting hydraulic simulation predicts the HGL (junction pressures) and pipe flows at a predetermined number of nodes (or data points) in the network. This information is then passed back to the associated calibration module. The calibration module evaluates how closely the model simulation is to the observed data, the calibration evaluation computes a goodness-of-fit value, which is the discrepancy between the observed data and the model predicted pipe flows and junction pressures or HGL, for each solution. This goodness-of-fit value is then assigned as the fitness for that solution in the genetic algorithm.

One generation produced by the genetic algorithm is then complete. The fitness measure is taken into account when performing the next generation of the genetic algorithm operations. To find the optimal calibration solutions, fitter solutions will be selected by mimicking Darwin's natural selection principle of survival of the fittest. The selected solutions are used to reproduce a next generation of calibration solutions by performing genetic operations. Over many generations, the solutions evolve, and the optimal or near optimal solutions ultimately emerge. There are numerous variations of genetic algorithms over the last decade. Many successful applications of GA to solving model calibrations have been carried out for optimized calibration of water resource systems (Wang 1992; Wu 1994; Babovic etc. 1994; Wu and Larsen 1996). More recently, a competent genetic algorithm (also called fast messy GA), which has been demonstrated the most efficient GA for the optimization of a water distribution system (Wu & Simpson 2001), has been used for the optimized calibration. A brief overview is given in the following section.