Simply modified GKL density classifiers that reach consensus faster
Cellular Automata and Lattice Gases
2019-05-27 v2 Statistical Mechanics
Dynamical Systems
Abstract
The two-state Gacs-Kurdyumov-Levin (GKL) cellular automaton has been a staple model in the study of complex systems due to its ability to classify binary arrays of symbols according to their initial density. We show that a class of modified GKL models over extended neighborhoods, but still involving only three cells at a time, achieves comparable density classification performance but in some cases reach consensus more than twice as fast. Our results suggest the time to consensus (relative to the length of the CA) as a complementary measure of density classification performance.
Cite
@article{arxiv.1904.07411,
title = {Simply modified GKL density classifiers that reach consensus faster},
author = {J. Ricardo G. Mendonça},
journal= {arXiv preprint arXiv:1904.07411},
year = {2019}
}
Comments
Short note, 3 pages, 1 table, 2 composite figures, 18 references