A Survey of Model-Based Methods for Global Optimization

author: Thomas Bartz-Beielstein, TH Köln (University of Applied Sciences)
published: May 31, 2016,   recorded: May 2016,   views: 139
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Description

This article describes model-based methods for global optimization. After introducing the global optimization framework, modeling approaches for stochastic algorithms are presented. We differentiate between models that use a distribution and models that use an explicit surrogate model. Fundamental aspects of and recent advances in surrogate-model based optimization are discussed. Strategies for selecting and evaluating surrogates are presented. The article concludes with a description of key features of two state-of-the-art surrogate model based algorithms, namely the evolvability learning of surrogates (EvoLS) algorithm and the sequential parameter optimization (SPO).

This lecture is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 692286.

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Reviews and comments:

Comment1 Adrian Lisko, June 12, 2016 at 9:21 a.m.:

Hello, is the presenter's jupyter notebook also available somewhere?

Thanks in advance.


Comment2 Adrian Lisko, June 12, 2016 at 9:21 a.m.:

Hello, is the presenter's jupyter notebook also available somewhere?

Thanks in advance.


Comment3 Alon Henson, July 11, 2016 at 4:22 p.m.:

You can find it in his publication here:
http://www.spotseven.de/wp-content/pa...

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