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Evolutionary investigations are often very expensive in terms of the required computational resources and many general questions regarding the utility of a feature F of an agent (e.g., in competitive environments) or the likelihood of F evolving (or not evolving) are therefore typically difficult, if not practically impossible to answer. We propose and demonstrate in extensive simulations a methodology that allows us to answer such questions in setups where good predictors of performance in a task T are available. These predictors evaluate the performance of an agent kind A in a task T*, which can then transformed by including costs and additional factors to make predictions about the performance of A in T.

Predicting population dynamics and evolutionary trajectories based on performance evaluations in alife simulations