Source:
Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems, MIT Press, Volume 1, Nara, Japan (1996)
URL:
http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf
Keywords:
genetic algorithms;
genetic programming
Abstract:
A cellular automata rule for the majority
classification task was evolved using genetic
programming with automatically defined functions. The
genetically evolved rule has an accuracy of 82.326%.
This level of accuracy exceeds that of the
Gacs-Kurdyumov-Levin (GKL) rule, all other known
human-written rules, and all other rules produced by
known previous automated approaches.
Our genetically evolved rule is qualitatively different
from other rules in that it uses a fine-grained
internal representation of density information; it
employs a large number of different domains and
particles; and it uses an intricate set of signals for
communicating information over large distances in time
and space.