abstract = "This paper compares the efficacy of different
crossover operators for Grammatical Evolution across a
typical numeric regression problem and a typical data
classification problem. Grammatical Evolution is an
extension of Genetic Programming, in that it is an
algorithm for evolving complete programs in an
arbitrary language. Each of the two main crossover
operators struggles (for different reasons) to achieve
100percent correct solutions. A mechanism is proposed,
allowing the evolutionary algorithm to self-select the
type of crossover used and this is shown to improve the
rate of generating 100percent successful solutions.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.