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Algorithmic Feasibility of Observing Artificial Life Evolution

An observation process is a fundamental implicit component of the simulation based studies on artificial-evolutionary systems (AES) by which time-varying entities are identified and their behavior is observed to uncover higher-level “emergent” phenomena. In this paper, we analyze algorithmic feasibility of implementing an observation process and consequent automated discovery of the entities and the evolutionary processes in arbitrary AES models. We characterize the bounds for the worst case computational complexity for the process of discovery of possible presence of entity and population level reproduction with epigenetic development in the child entities involving mutations and heredity in presence of natural selection. In particular, we prove that if entities in an AES simulation are structurally distinguishable, the problem of observability of evolutionary processes is only polynomially harder w.r.t. the entity recognition. The complexity bounds are presented in parameterized form so that for any given AES model, if parameter estimates are known, corresponding bounds can be derived.