A survey and taxonomy of performance improvement of canonical genetic programming

The genetic programming (GP) paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has been applied with breakthrough success in various scientific and engineering applications. However, one of the main drawbacks of GP has been the often large amount of computational effort required to solve complex problems. Much disparate research has been conducted over the past 25 years to devise innovative methods to improve the efficiency and performance of GP. This paper attempts to provide a comprehensive overview of this work related to Canonical Genetic Programming based on parse trees and originally championed by Koza (Genetic programming: on the programming of computers by means of natural selection. MIT, Cambridge, 1992). Existing approaches that address various techniques for performance improvement are identified and discussed with the aim to classify them into logical categories that may assist with advancing further research in this area. Finally, possible future trends in this discipline and some of the open areas of research are also addressed.