Evolving memory: Logical tasks for cellular automata

We present novel experiments in the evolution of Cellular Automata (CA) to solve nontrivial tasks. Using a genetic algorithm, we evolved CA rules that can solve non-trivial logical tasks related to the density task (or majority classification problem) commonly used in the literature. We present the particle catalogs of the new rules following the computational mechanics framework. We know from Crutchfield et al (2002) that particle computation in CA is a process of information processing and integration. Here, we discuss the type of memory that emerges from the evolving CA experiments for storing and manipulating information, In particular, we contrast this type of evolved memory with the type of memory we are familiar with in Computer Science, and also with the type of biological memory instantiated by DNA. A novel CA rule obtained from our own experiments is used to elucidate the type of memory that one-dimensional CA can attain.