@conference { andre:1996:camc,
	title = {Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem},
	editor = {John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo},
	year = {1996},
	month = {"28--31 " # jul},
	pages = {3--11},
	publisher = {MIT Press},
	type = {inproceedings},
	address = {Stanford University, CA, USA},
	abstract = {It is difficult to program cellular automata. This is
                 especially true when the desired computation requires
                 global communication and global integration of
                 information across great distances in the cellular
                 space. Various human- written algorithms have appeared
                 in the past two decades for the vexatious majority
                 classification task for one-dimensional two-state
                 cellular automata. This paper describes how genetic
                 programming with automatically defined functions
                 evolved a rule for this task with an accuracy of
                 82.326%. This level of accuracy exceeds that of the
                 original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all
                 other known human-written rules, and all other known
                 rules produced by automated methods. The rule evolved
                 by genetic programming is qualitatively different from
                 all previous rules in that it employs a larger and more
                 intricate repertoire of domains and particles to
                 represent and communicate information across the
                 cellular space.},
	keywords = {genetic algorithms, genetic programming},
	URL = {http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf},
	author = { Andre, David  and  III, Forrest H Bennett  and  Koza, John R.}
}