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Momodou L. Sanyang, Ata Kabán. Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles[J]. Journal of Computer Science and Technology, 2019, 34(6): 1241-1257. DOI: 10.1007/s11390-019-1973-1
Citation: Momodou L. Sanyang, Ata Kabán. Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles[J]. Journal of Computer Science and Technology, 2019, 34(6): 1241-1257. DOI: 10.1007/s11390-019-1973-1

Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles

  • 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 require (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 flexible approach to balance exploration versus exploitation. We perform a thorough experimental study on high-dimensional benchmark functions, and provide statistical analyses that demonstrate the state-of-the-art performance of our approach when compared with existing alternatives in problems with 1 000 search variables.
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