%0 Conference Paper %1 andre:1996:camc %A Andre, David %A Bennett III, Forrest H %A Koza, John R. %B Genetic Programming 1996: Proceedings of the First Annual Conference %C Stanford University, CA, USA %D 1996 %E Koza, John R. %E Goldberg, David E. %E Fogel, David B. %E Riolo, Rick L. %I MIT Press %K algorithms, genetic programming %P 3--11 %T Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem %U http://cognet.mit.edu/library/books/view?isbn=0262611279 %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.

@inproceedings{andre:1996:camc, abstract = {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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Stanford University, CA, USA}, author = {Andre, David and {Bennett III}, Forrest H and Koza, John R.}, biburl = {https://www.bibsonomy.org/bibtex/2b60b6f16beae270f65480b3a7487fd80/brazovayeye}, booktitle = {Genetic Programming 1996: Proceedings of the First Annual Conference}, editor = {Koza, John R. and Goldberg, David E. and Fogel, David B. and Riolo, Rick L.}, interhash = {5a32c0b6da9b584ea5f33d5e8bb5881c}, intrahash = {b60b6f16beae270f65480b3a7487fd80}, keywords = {algorithms, genetic programming}, month = {28--31 July}, notes = {GP-96}, pages = {3--11}, publisher = {MIT Press}, size = {9 pages}, timestamp = {2008-06-19T17:35:43.000+0200}, title = {Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem}, url = {http://cognet.mit.edu/library/books/view?isbn=0262611279}, year = 1996 }