How to Mislead an Evolutionary Algorithm Using Global Sensitivity Analysis

  • Thomas Chabin
  • Alberto Tonda
  • Evelyne Lutton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9554)


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.


Pareto Front Optimal Pareto Front Global Sensitivity Analysis Covariance Matrix Adaptation Evolution Strategy Stochastic Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.UMR 782 GMPA, INRAThiverval-GrignonFrance

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