TY - JOUR
T1 - Random expansion method for the generation of complex cellular automata
AU - Seck-Tuoh-Mora, Juan Carlos
AU - Hernandez-Romero, Norberto
AU - Medina-Marin, Joselito
AU - Martinez, Genaro J.
AU - Barragan-Vite, Irving
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/3
Y1 - 2021/3
N2 - The complex behaviors in cellular automata have been widely developed in recent years to generate and analyze automata that produce space-moving patterns or gliders that interact in a periodic background. This type of automata has been frequently found either by conducting an exhaustive search or through a meticulous construction of the evolution rule. In this study, the specification of cellular automata with complex behaviors was obtained by utilizing randomly generated specimens. In particular, it was proposed that a cellular automaton of n states should be specified at random and then extended to another automaton with a higher number of states so that the original automaton operates as a periodic background where the additional states serve to define the gliders. Moreover, this study presents an explanation of this method. Furthermore, the random way of defining complex cellular automata was studied by using mean-field approximations for various states and local entropy measures. This specification was refined with a genetic algorithm to obtain specimens of a higher degree of complexity. By adopting this methodology, it was possible to generate complex automata with hundreds of states, demonstrating the fact that randomly defined local interactions with multiple states can construct complexity.
AB - The complex behaviors in cellular automata have been widely developed in recent years to generate and analyze automata that produce space-moving patterns or gliders that interact in a periodic background. This type of automata has been frequently found either by conducting an exhaustive search or through a meticulous construction of the evolution rule. In this study, the specification of cellular automata with complex behaviors was obtained by utilizing randomly generated specimens. In particular, it was proposed that a cellular automaton of n states should be specified at random and then extended to another automaton with a higher number of states so that the original automaton operates as a periodic background where the additional states serve to define the gliders. Moreover, this study presents an explanation of this method. Furthermore, the random way of defining complex cellular automata was studied by using mean-field approximations for various states and local entropy measures. This specification was refined with a genetic algorithm to obtain specimens of a higher degree of complexity. By adopting this methodology, it was possible to generate complex automata with hundreds of states, demonstrating the fact that randomly defined local interactions with multiple states can construct complexity.
KW - Cellular automata
KW - Complexity
KW - Entropy
KW - Local information
KW - Mean-field theory
UR - http://www.scopus.com/inward/record.url?scp=85097747820&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2020.11.041
DO - 10.1016/j.ins.2020.11.041
M3 - Artículo
AN - SCOPUS:85097747820
SN - 0020-0255
VL - 549
SP - 310
EP - 327
JO - Information Sciences
JF - Information Sciences
ER -