 The task is far from trivial in a CA, involving the intricacy of the emergence in both  E2  and  E3  sense. The answer requires a global perspective (how many cells are white (black) in the lattice as a whole?). However, the cells work with only local rules explicitly encoded in them: no single cell can do the counting. The ideal automaton should find a way to aggregate information from several parts of its own lattice to give the final answer. A kind of emergent computation is needed to successfully solve this density classification task. It has been proved that no CA can solve this problem precisely (see Land &amp; Belew 1995). Since, however, CA with larger neighborhoods achieve better results in tasks of this kind, genetic algorithms are used to efficiently search the solution space (genetic algorithms are computational analogues of genetic evolution, in which a computational problem is addressed by producing, combining and selecting solutions to it; the details of the procedure as applied to the classification problem are not important for our purpose, but, for an accessible presentation, see Mitchell 2009: 22; for a general introduction see Mitchell 1998). The following is a diagram of the “Rule \(\phi_{17083}\)”, discovered by James Crutchfield and Melanie Mitchell (1995). The CA implementing the rule starts from an initial state with more white cells: 