  When seeking a predictive model in biomedical data, one often has more than a
single objective in mind, e.g., attaining both high accuracy and low complexity
(to promote interpretability). We investigate herein whether multiple
objectives can be dynamically tuned by our recently proposed coevolutionary
algorithm, SAFE (Solution And Fitness Evolution). We find that SAFE is able to
automatically tune accuracy and complexity with no performance loss, as
compared with a standard evolutionary algorithm, over complex simulated
genetics datasets produced by the GAMETES tool.

Neural and Evolutionary Computing