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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Springer US

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DOI

10.1016/0898-1221(87)90124-6

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1573-1375

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Complement to a comparative analysis of heuristics

Authors: Pierre Hansen Nenad Mladenović
Publish Date: 2007/08/22
Volume: 18, Issue: 1, Pages: 41-46
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Abstract

A recent comparison of evolutionary neural network and scatter search heuristics for solving the pmedian problem is completed by i gathering or obtaining exact optimal values in order to evaluate errors precisely and ii including results obtained with several variants of a variable neighborhood search VNS heuristic For a first wellknown series of instances the average errors of the evolutionary and neural network heuristics are over 10 and more than 1000 times larger than that of VNS For a second series this error is about 3 while the errors of the parallel VNS and of a hybrid heuristic are about 001 and that of parallel scatter search even smaller

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  2. A focused information criterion for graphical models
  3. An advancement in clustering via nonparametric density estimation
  4. Clustering via nonparametric density estimation
  5. Sequential importance sampling of binary sequences
  6. Deletion diagnostics for marginal mean and correlation model parameters in estimating equations
  7. Boosted coefficient models
  8. Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
  9. Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification
  10. Gaussian proposal density using moment matching in SMC methods
  11. Bayesian inference for Heston-STAR models
  12. Parsimonious Gaussian mixture models
  13. Efficient estimation of variance components in nonparametric mixed-effects models with large samples
  14. On the computation of the noncentral F and noncentral beta distribution
  15. A new variable importance measure for random forests with missing data
  16. Likelihood-free parallel tempering
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