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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.

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Stanford Exploration Project

11/11/2002