%0 Conference Paper
%B Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems
%D 1996
%T Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming
%A Andre, David
%A III, Forrest Bennett
%A Koza, John
%C Nara, Japan
%I MIT Press
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf
%V 1
%X 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.
%8 "16--18 " # may
%9 inproceedings