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Communication Dans Un Congrès Année : 2015

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.
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Dates et versions

hal-01366572 , version 1 (14-09-2016)

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Thomas Chabin, Alberto Tonda, Evelyne Lutton. Is global sensitivity analysis useful to evolutionary computation?. 17. Genetic and Evolutionary Computation Conference (GECCO), Jul 2015, Madrid, Spain. ⟨10.1145/2739482.2764675⟩. ⟨hal-01366572⟩
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