Abstract
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.
This work has been funded by the French National Agency for research (ANR), under the grant ANR-11-EMMA-0017, EASEA-Cloud Emergence project 2011, http://www.agence-nationale-recherche.fr/.
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Chabin, T., Tonda, A., Lutton, E. (2016). How to Mislead an Evolutionary Algorithm Using Global Sensitivity Analysis. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_4
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