17154fd6bb
Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the 'jDE' algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
6 lines
416 B
Text
6 lines
416 B
Text
$NetBSD: distinfo,v 1.1 2019/08/09 15:43:48 brook Exp $
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SHA1 (R/DEoptimR_1.0-8.tar.gz) = 3974d3642d4426fa2ee2093c13cae7aeba2dd70f
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RMD160 (R/DEoptimR_1.0-8.tar.gz) = 7d8672d59375f1fc3b758032bd2920287829d787
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SHA512 (R/DEoptimR_1.0-8.tar.gz) = d1ff9e4b12619df383cf01da48b084e5f88c5f33a7d943c1625c25345ecc6913a0879c54542d8507c7ec261a29bac1e18a913b9efb46775f259cf48481814515
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Size (R/DEoptimR_1.0-8.tar.gz) = 35401 bytes
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