in selecting two parents among its neighbors according to a certain criterion, applying the variation operators to them (recombination and mutation for example), and replacing the considered individual by the recently created offspring following a given criterion, for instance, replace if the offspring represents a better solution than the considered individual. Synchronous versus asynchronous In a regular synchronous cEA, the algorithm proceeds from the very first top left individual to the right and then to the several rows by using the information in the population to create a new temporary population. After finishing with the bottom-right last individual the temporary population is full with the newly computed individuals, and the replacement step starts. In it, the old population is completely and synchronously replaced with the newly computed one according to some criterion. Usually, the replacement keeps the best individual in the same position of both populations, that is, elitism is used. We must notice that according to the update policy of the population used, we could also define an asynchronous cEA. This is also a well-known issue in cellular automata. In asynchronous cEAs the order in which the individuals in the grid are update changes depending on the criterion used: line sweep, fixed random sweep, new random sweep, and uniform choice. These are the four most usual ways of updating the population. All of them keep using the newly computed individual (or the original if better) for the computations of its neighbors immediately. This makes the population to hold at any time individual in different states of evolution, defining a very interesting new line of research. The overlap of the neighborhoods provides an implicit mechanism of solution migration to the cEA. Since the best solutions spread smoothly through the whole population, genetic diversity in the population is preserved longer than in non structured EAs. This soft dispersion of the best solutions through the population is one of the main issues of the good tradeoff between exploration and exploitation that cEAs perform during the search. It is then easy to see that we could tune this tradeoff (and hence, tune the genetic diversity level along the evolution) by modifying (for instance) the size of the neighborhood used, as the overlap degree between the neighborhoods grows according to the size of the neighborhood. A cEA can be seen as a cellular automaton (CA) with probabilistic rewritable rules, where the alphabet of the