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Simple Summary  We present a program that simulates the coevolution of digital organisms competing for limited resources. The rules of competition and collaboration implicit in the code have been inspired by the underlying mechanisms that generate natural selection. The simplest, most general, and least controversial rules presented in natural systems have been chosen, and these rules have been implemented in the most flexible and adaptable way found. Our model is capable of (i) measuring the evolutionarily stable equilibrium or the unstable imbalance of biological interactions, (ii) weighing biological competition and cooperation relationships to evaluate their relative incidence in the coevolution of species, (iii) verifying widely contrasted evolutionary theories with this instrument, and (iv) shedding light on the evolutionary effects of other mechanisms, such as the spatial distribution of populations, that have not been studied in depth before.    Abstract  This paper presents an Evolutionary Cellular Automaton (ECA) that simulates the evolutionary dynamics of biological interactions by manipulating strategies of dispersion and associations between digital organisms. The parameterization of the different types of interaction and distribution strategies using configuration files generates easily interpretable results. In that respect, ECA is an effective instrument for measuring the effects of relative adaptive advantages and a good resource for studying natural selection. Although ECA works effectively in obtaining the expected results from most well-known biological interactions, some unexpected effects were observed. For example, organisms uniformly distributed in fragmented habitats do not favor eusociality, and mutualism evolved from parasitism simply by varying phenotypic flexibility. Finally, we have verified that natural selection represents a cost for the emergence of sex by destabilizing the stable evolutionary strategy of the 1:1 sex ratio after generating randomly different distributions in each generation.

A Study of the Coevolution of Digital Organisms with an Evolutionary Cellular Automaton