%0 Conference Paper
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%T Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem
%A Andre, David
%A III, Forrest Bennett
%A Koza, John
%C Stanford University, CA, USA
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%I MIT Press
%K genetic algorithms, genetic programming
%P 3--11
%U http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf
%X 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.
%8 "28--31 " # jul
%9 inproceedings