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The ideal convergence criterion for a genetic algorithm would be one that
guaranteed that each and all of the parameters converge independently
Beasley et al. (1993a); Goldberg (1989). However, this may be too demanding or may result in
too many iterations, so more relaxed convergence criteria are usually employed.
Here I used four convergence criteria:
- 1.
- The fitness function value must be below a given threshold value.
- 2.
- The difference between the best and the average fitness is less than a
given fraction of the fitness of the average individual.
- 3.
- The difference between the best individual of the current population and
the best individual so far must be very small (even zero). This means that the
most fit individual has converged even if the population itself has not.
- 4.
- the number of iterations (generations of the sample
population) exceeds a given limit. This prevents the algorithm from spending
too much time refining an existing solution.

The combination of these criteria is intended to guarantee that the solution is
not due to a lucky guess of the random generator but to a comprehensive search of
the model space.

** Next:** Inversion Constraints
** Up:** Velocity Inversion
** Previous:** Fitness function
Stanford Exploration Project

11/11/2002