abstract = "Performance of Genetic Programming depends its genetic
operators, especially the crossover operator. The
simple crossover randomly swaps subtrees of the
parents. The {"}good{"} subtree can be destroyed by an
inappropriate choice of the crossover point. This work
proposes a crossover operator that identifies a good
subtree by measuring its impact on the fitness value
and recombines good subtrees from parents. The proposed
operator, called selective crossover, has been tested
on two problems with satisfactory results.",