Category Archives: Sequential Parameter Optimization

Free Online-Version: Metamodel-based optimization of hot rolling processes in the metal industry


The article “Metamodel-based optimization of hot rolling processes in the metal industry” is published in The International Journal of Advanced Manufacturing. As part of the Springer Nature SharedIt initiative, a publicly full-text view-only version of this paper is available here.

The abstract reads as follows: Continue reading

Successful Presentations @useR!2017 Brussels

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Last week the useR! 2017 conference took place in Brussels, Belgium. The annual useR! conference is the main meeting of the international R user and developer community. Over 1000 participants came to listen to a broad spectrum of talks ranging from technical and R-related computing issues to general statistical topics of current interest .

Here you find some impressions from the conference: Pictures

The SPOTSeven team gave two presentations:

Here you can find the slides:

The talks enjoyed great participation and resulted in lots of interesting discussions afterwards.

Last chance: Free access to article about Model-based Methods #optimization

If you are interested in “Model-based Methods for Continuous and Discrete Global Optimization“, you can freely access the article until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2
The SPO Toolbox was used for performing the experiments described in this article. The Sequential Parameter Optimization Toolbox 2.0.1 is a major update of the SPOT R package. It provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: https://CRAN.R-project.org/package=SPOT

Interested in #ModelBased Methods for #Optimization? #SPOT2

If you are interested in “Model-based Methods for Continuous and Discrete Global Optimization“, you can freely access the article until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2
The SPO Toolbox was used for performing the experiments described in this article. The Sequential Parameter Optimization Toolbox 2.0.1 is a major update of the SPOT R package. It provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: https://CRAN.R-project.org/package=SPOT

Sequential Parameter #Optimization Toolbox SPOT 2.0.1 on CRAN #rstats

SPOT: Sequential Parameter Optimization Toolbox 2.0.1

This is a major update of the R package. The SPO toolbox provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: https://CRAN.R-project.org/package=SPOT

Click and Read! Free Access: Continuous and Discrete Global #Surrogate #Optimization

http://dx.doi.org/10.1016/j.asoc.2017.01.039Just click and read! Everybody can use the following personal article link, which will provide free access to the article “Model-based methods for continuous and discrete global optimization” (Thomas Bartz-Beielstein, Martin Zaefferer), and is valid for 50 days, until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2

Here are some highlights:

  • Up-to-date survey and comprehensive taxonomy of surrogate model based optimization algorithms.
  • Covers continuous and discrete/combinatorial search spaces.
  • Presents six strategies for dealing with discrete data structures.
  • New strategy for model selection and combination in surrogate model-based optimization.
  • Outlook on important challenges (model selection, dimensionality, benchmarks, definiteness) and research directions.

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