ABSTRACT
This chapter describes some details of the genetic algorithm (GA) and how the GA is used to solve the problem. The GA details include data representation, operators, and fitness functions. The application’s fitness function is a measure of how well the candidate solution fits the data. The procedure for shrinking parameter intervals attempts to find new upper and lower bounds based on the parameter values found in the GA runs made with the previous bounds. The version of the software shown in the following listing uses the least-squares criterion, but it is simple to change to a different criterion. Only slight program changes are needed to use a different fit function or goodness-of-fit criterion. The GA approach may be a useful alternative in those cases. Poor estimates may preclude convergence with many techniques, but the GA technique can probably “discover” this fact and adjust its intervals to find the correct values.
