%0 Conference Paper %1 andre:1996:GKL %A Andre, David %A Bennett III, Forrest H %A Koza, John R. %B Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems %C Nara, Japan %D 1996 %I MIT Press %K algorithms, genetic programming %T Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using 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.

@inproceedings{andre:1996:GKL, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Nara, Japan}, author = {Andre, David and {Bennett III}, Forrest H and Koza, John R.}, biburl = {https://www.bibsonomy.org/bibtex/2ec6fc70987869c6ae9985014791fd8e5/brazovayeye}, booktitle = {Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems}, interhash = {ec9f52b204d909c7d9adcaa3a3460554}, intrahash = {ec6fc70987869c6ae9985014791fd8e5}, keywords = {algorithms, genetic programming}, month = {16--18 May}, notes = {Alife-5 A longer version of this paper will be presented at the GP-96 conference. GP gets best solution to GKL problem {"}The population size used to evolve the current world's record for the GKL majority classification 1-dimensionall 2-sate 7-neighbor cellular authomata problem was 51,200. I believe Melanie Mitchell at the Santa Fe Institute has been doing continuing additional work on using GAs to evolve CA rules for various other problems.{"}}, publisher = {MIT Press}, publisher_address = {Cambridge, MA, USA}, size = {10 pages}, timestamp = {2008-06-19T17:35:43.000+0200}, title = {Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming}, url = {http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf}, volume = 1, year = 1996 }