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.
Users
Please
log in to take part in the discussion (add own reviews or comments).