Generalized multiple attractor cellular automata (GMACA) model for associative memory
This paper reports an efficient technique of evolving Cellular Automata (CA) as an associative memory model. The evolved CA termed as GMACA (Generalized Multiple Attractor Cellular Automata), acts as a powerful pattern recognizer. Detailed analysis of GMACA rules establishes the fact that the rule subspace of the pattern recognizing CA lies at the edge of chaos - believed to be capable of executing complex computation.
cellular automata edge of chaos ga pattern recognition