How to Mislead an Evolutionary Algorithm Using Global Sensitivity Analysis.

The idea of exploiting Global Sensitivity Analysis GSA to make Evolutionary Algorithms more effective seems very attractive: intuitively, a probabilistic analysis can prove useful to a stochastic optimisation technique. GSA, that gathers information about the behaviour of functions receiving some inputs and delivering one or several outputs, is based on computationally-intensive stochastic sampling of a parameter space. Nevertheless, efficiently exploiting information gathered from GSA might not be so straightforward. In this paper, we present three mono- and multi-objective counterexamples to prove how naively combining GSA and EA may mislead an optimisation process.