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The fitness function defines the criterion for ranking potential hypotheses and for probabilistically selecting them for inclusion in the next-generation population.
Suppose the task is to learn classification rules. In that case, the fitness function typically has a component that scores the classification accuracy of the rule over a set of provided training examples.
Often other criteria may be included as well, such as the complexity or generality of the rule. More generally, when the bit-string hypothesis is interpreted as a complex procedure (e.g., when the bit string represents a collection of if-then rules that will be chained together to control a robotic device), the fitness function may measure the overall performance of the resulting procedure rather than the performance of individual rules.