An Analysis of Semantic Aware Crossover

It is well-known that the crossover operator plays an important role in Genetic Programming (GP). In Standard Crossover (SC), semantics are not used to guide the selection of the crossover points, which are generated randomly. This lack of semantic information is the main cause of destructive effects from SC (e.g., children having lower fitness than their parents). Recently, we proposed a new semantic based crossover known GP called Semantic Aware Crossover (SAC) [25]. We show that SAC outperforms SC in solving a class of real-value symbolic regression problems. We clarify the effect of SAC on GP search in increasing the semantic diversity of the population, thus helping to reduce the destructive effects of crossover in GP.

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