A Comparative Analysis of GP
Advances in Genetic Programming 2, chapter 2, MIT Press, Cambridge, MA, USA, (1996)Abstract
In order to analyze Genetic Programming (GP), this
chapter compares it with two alternative adaptive
search algorithms, Simulated Annealing (SA) and
Stochastic Iterated Hill Climbing (SIHC). SIHC and SA
are used to solve program discovery problems posed in
the style of GP. In separate versions they employ
either GP's crossover operator or a mutation operator.
The comparisons in terms of likelihood of success and
efficiency show them to be effective. Based upon their
success, hybrid versions of GP and hill climbing are
designed that improve upon a canonical version of GP.
Program discovery practitioners may find it useful to
coherently view all the algorithms this chapter
considers by using the perspective of evolution.