Coevolution has been used as optimization technique both successfully and unsuccessfully. Successful optimization shows integration of information at the individual level over many fitness evaluation events and over many generations. Alternative outcomes of the evolutionary process, e.g. red queen dynamics or speciation, prevent such integration. Why coevolution leads to integration of information or to alternative evolutionary outcomes is generally unclear. We study coevolutionary optimization of the density classification task in cellular automata in a spatially explicit, two-species model. We find optimization at the individual level, i.e. evolution of cellular automata that are good density classifiers. However, when we globally mix the populations, which prevents the formation of spatial patterns, we find typical red queen dynamics in which cellular automata classify all cases to a single density class regardless their actual density. Thus, we get different outcomes of the evolutionary process dependent on a small change in the model. We compare the two processes leading to the different outcomes in terms of the diversity of the two populations at the level of the genotype and at the level of the phenotype.
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