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.
@inproceedings{scheutzschermerhorn05gecco,
title={Predicting Population Dynamics and Evolutionary Trajectories based on Performance Evaluations in Alife Simulations},
author={Matthias Scheutz and Paul Schermerhorn},
year={2005},
month={June},
booktitle={Proceedings of GECCO 2005},
publisher={ACM Press},
pages={35--42}
url={https://hrilab.tufts.edu/publications/scheutzschermerhorn05gecco.pdf}
}