Created by W.Langdon from gp-bibliography.bib Revision:1.5251
This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement.
A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases.
We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and that it works harmoniously with two other well known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method.",
Disappeared Sep 2014 http://casnew.iti.upv.es/index.php/evocompetitions/105-symregcompetition",
Genetic Programming entries for R Muhammad Atif Azad Conor Ryan