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The evolutionary operators are iteratively applied in an EA until a stopping condition is satis ed The simplest stopping condition is to limit the number of generations that the EA is allowed to execute, or alternatively, a limit is placed on the number of tness function evaluations This limit should not be too small, otherwise the EA will not have su cient time to explore the search space In addition to a limit on execution time, a convergence criterion is usually used to detect if the population has converged Convergence is loosely de ned as the event when the population becomes stagnant In other words, when there is no genotypic or phenotypic change in the population The following convergence criteria can be used: Terminate when no improvement is observed over a number of consecutive generations This can be detected by monitoring the tness of the best individual If there is no signi cant improvement over a given time window, the EA can be stopped Alternatively, if the solution is not satisfactory, mechanisms can be applied to increase diversity in order to force further exploration For example, the mutation probability and mutational step sizes can be increased Terminate when there is no change in the population If, over a number of consecutive generations, the average change in genotypic information is too small, the EA can be stopped Terminate when an acceptable solution has been found If x (t) represents the optimum of the objective function, then if the best individual, xi , is such that f (xi ) |f (x) |, an acceptable solution is found; is the error threshold If is too large, solutions may be bad Too small values of may cause the EA never to terminate if a time limit is not imposed Terminate when the objective function slope is approximately zero, as de ned in equation (1616) of 16.

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The evolutionary operators are iteratively applied in an EA until a stopping condition is satis ed The simplest stopping condition is to limit the number of generations that the EA is allowed to execute, or alternatively, a limit is placed on the number of tness function evaluations This limit should not be too small, otherwise the EA will not have su cient time to explore the search space In addition to a limit on execution time, a convergence criterion is usually used to detect if the population has converged Convergence is loosely de ned as the event when the population becomes stagnant In other words, when there is no genotypic or phenotypic change in the population The following convergence criteria can be used: Terminate when no improvement is observed over a number of consecutive generations This can be detected by monitoring the tness of the best individual If there is no signi cant improvement over a given time window, the EA can be stopped Alternatively, if the solution is not satisfactory, mechanisms can be applied to increase diversity in order to force further exploration For example, the mutation probability and mutational step sizes can be increased Terminate when there is no change in the population If, over a number of consecutive generations, the average change in genotypic information is too small, the EA can be stopped Terminate when an acceptable solution has been found If x (t) represents the optimum of the objective function, then if the best individual, xi , is such that f (xi ) |f (x) |, an acceptable solution is found is the error threshold If is too large, solutions may be bad Too small values of may cause the EA never to terminate if a time limit is not imposed Terminate when the objective function slope is approximately zero, as de ned in equation (1616) of 16.

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