Parameter Selection

The first step, of course, is to select the model parameters related to the ``physics'' of the problem, their ranges (maximum and minimum values) and their required resolution. Then it is time to choose the evolution parameters actually related to the genetic algorithm itself. The performance of a genetic algorithm to solve a particular optimization problem depends critically on the choice of its evolution parameters that must be fine-tuned to that problem as much as possible. In general it is difficult to give hard and fast rules that may work with a wide range of applications, although some guidelines exist Goldberg and Deb (1991); Goldberg and Richardson (1987); Goldberg (1989b). For this problem, I choose the evolution parameters one by one starting with the most critical and working my way down to the least critical. Once a particular parameter is selected it is kept constant in the tests to select the remaining parameters. I do so because it would be nearly impossible to test all possible combinations. The reader may find useful to refer to Appendix A for a description of the evolution parameters themselves.