activity
20142026
most citedBenchmarking in Optimization: Best Practice and Open Issues

82 citations · 247 across the 54 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

cs.AI2022★ 1 cited

OPTION: OPTImization Algorithm Benchmarking ONtology

Ana Kostovska, Diederick Vermetten, Carola Doerr +3

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use diff…

cs.LG2022★ 1 cited

Explainable Model-specific Algorithm Selection for Multi-Label Classification

Ana Kostovska, Carola Doerr, Sašo Džeroski +3

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest i…

cs.NE2022★ 5 cited

Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj +6

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available compu…

cs.NE2022★ 21 cited

SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison

Gjorgjina Cenikj, Ryan Dieter Lang, Andries Petrus Engelbrecht +3

Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios. In thi…

cs.NE2022★ 1 cited

The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants

Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3

Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm…

cs.NE2022★ 2 cited

Per-run Algorithm Selection with Warm-starting using Trajectory-based Features

Ana Kostovska, Anja Jankovic, Diederick Vermetten +4

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform…