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Conference Papers Year : 2015

Variance Reduction in Population-Based Optimization: Application to Unit Commitment

Abstract

We consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, which is relevant for most noisy optimization problems. We present artificial models of noise for which common random numbers are very efficient, and artificial models of noise for which common random numbers are detrimental. We then experiment on a desperately expensive unit commitment problem. As expected, stratified sampling is never detrimental. Nonetheless, in practice, common random numbers provided, by far, most of the improvement .
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Dates and versions

hal-01194510 , version 1 (07-09-2015)

Identifiers

  • HAL Id : hal-01194510 , version 1

Cite

Jean-Joseph Christophe, Jérémie Decock, Jialin Liu, Olivier Teytaud. Variance Reduction in Population-Based Optimization: Application to Unit Commitment. Artificial Evolution (EA2015), 2015, Lyon, France. ⟨hal-01194510⟩
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