Large scale estimation of distribution algorithms with adaptive heavy tailed random projection ensembles

Momodou Sanyang, Ata Kaban

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We present new variants of Estimation of Distribution Algorithms (EDA) for large scale continuous optimisation, that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here
is to depart from the theory of RPs that required (sub-)Gaussian random matrices for norm-preservation, and instead for the purposes of high dimensional search we propose to employ random matrices with independent and identically distributed entries drawn from a t-distribution. We analytically show that the implicitly resulting high dimensional covariance of the search distribution is enlarged as a result. Moreover, the extent of this enlargement is controlled by a single parameter, the degree of freedom. For this reason, in the context of optimisation, such heavy tailed random matrices turn out to be preferable over the previously employed (sub-)Gaussians. Based on this observation, we then propose novel covariance adaptation schemes that are able to adapt the degree of freedom parameter during the search, and give rise to a exible approach to balance exploration versus exploitation. We perform a thorough experimental study on high dimensional benchmark functions, and provide statistical analyses that demonstrate state-of-the-art performance of our approach when compared with previously existing alternatives in problems with 1000 search variables.
Original languageEnglish
Pages (from-to)1241-1257
Number of pages21
JournalJournal of Computer Science and Technology
Issue number6
Publication statusPublished - 16 Nov 2019


  • covariance adaptation
  • estimation of distribution algorithm
  • random projection ensemble
  • t-distribution


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