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In genetic programming, crossover swaps randomly selected subtrees between parents. Typically, the probability of selecting a leaf as the subtree to be swapped is reduced, supposedly to allow larger structures on average. This paper reports on a study to determine the effect of modifying the leaf selection frequency for subtree crossover on the performance of a non-standard genetic program. Both a variety of constant values and dynamic update methods are investigated. It is shown that the performance of the genetic program is impacted by the manipulation of the leaf selection frequency and often can be improved using a random process rather than a constant value.