Titlebook: Experimental Methods for the Analysis of Optimization Algorithms; Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve

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The Sequential Parameter Optimization Toolboxactical and theoretical optimization problems. We describe the mechanics and interfaces employed by SPOT to enable users to plug in their own algorithms. Furthermore, two case studies are presented to demonstrate how SPOT can be applied in practice, followed by a discussion of alternative metamodels

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Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interaild a response surface model and use this model for finding good parameter settings of the given algorithm. We evaluated two methods from the literature that are based on Gaussian process models: sequential parameter optimization (SPO) (Bartz-Beielstein et al. 2005) and sequential Kriging optimizati

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David R. Barraclough,Angelo De Santisysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances.

Infant

https://doi.org/10.1007/978-94-007-0403-9 and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.

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Yu Li,Jonathan Li,Michael A. Chapmantechnique and discuss an extension of the initial . algorithm, which leads to a family of algorithms that we call iterated .. Experimental results comparing one specific implementation of iterated . to the original . algorithm confirm the potential of this family of algorithms.

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Algorithm Survival Analysisysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances.

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