abstract = "Genetic Programming (GP) is one of the evolutionary
computation (EC) methods followed with great interest
by many researchers. When GP first appeared, it has
become a popular computational intelligence method
because of its successful applications and its
potentials to find effective solutions for difficult
practical problems of many different disciplines. With
the use of GP in a wide variety of areas, numerous
variants of GP methods have emerged to provide more
effective solutions for computation problems of diverse
application fields. Therefore, GP has a very rich
literature that is progressively growing. Many GP
software tools developed along with process of GP
algorithms. There is a need for an inclusive survey of
GP literature from the beginning to today of GP in
order to reveal the role of GP in the computational
intelligence field. This survey study aims to provide
an overview of the growing GP literature in a
systematic way. The researchers, who need to implement
GP methods, can gain insight of potentials in GP
methods, their essential drawbacks and prevalent
superiorities. Accordingly, taxonomy of GP methods is
given by a systematic review of popular GP methods. In
this manner, GP methods are analyzed according to two
main categories, which consider the discrepancies in
their program (chromosome) representation styles and
their methodologies. Besides, GP applications in
diverse problems are summarized. This literature survey
is especially useful for new researchers to gain the
required broad perspective before implementing a GP
method in their problems.",