Is global sensitivity analysis useful to evolutionary computation?
Résumé
Global Sensitivity Analysis (GSA) studies how uncertainty in the inputs of a system influences uncertainty in its outputs. GSA is extensively used by experts to gather information about the behavior of models, through computationally-intensive stochastic sampling of parameters' space. Some studies propose to make use of the considerable quantity of data acquired in this way to optimize the model parameters, often resorting to Evolutionary Algorithms (EAs). Nevertheless, efficiently exploiting information gathered from GSA might not be so straightforward. In this paper, we present a counterexample followed by experimental results to prove how naively combining GSA and EA can bring about negative outcomes.