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Genetic programming is a powerful heuristic search technique that is used fora number of real world applications to solve among others regression,classification, and time-series forecasting problems. A lot of progress towardsa theoretic description of genetic programming in form of schema theorems hasbeen made, but the internal dynamics and success factors of genetic programmingare still not fully understood. In particular, the effects of differentcrossover operators in combination with offspring selection are largelyunknown.  This contribution sheds light on the ability of well-known GP crossoveroperators to create better offspring when applied to benchmark problems. Weconclude that standard (sub-tree swapping) crossover is a good default choicein combination with offspring selection, and that GP with offspring selectionand random selection of crossover operators can improve the performance of thealgorithm in terms of best solution quality when no solution size constraintsare applied.

On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection