activity
20162025
most citedBenchmarking in Optimization: Best Practice and Open Issues

82 citations · 106 across the 10 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.NE2020★ 7 cited

Identifying Properties of Real-World Optimisation Problems through a Questionnaire

Koen van der Blom, Timo M. Deist, Vanessa Volz +5

Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences. However, it is not clear how closely benchmarks match the properties of rea…

cs.AI2020

Towards Game-Playing AI Benchmarks via Performance Reporting Standards

Vanessa Volz, Boris Naujoks

While games have been used extensively as milestones to evaluate game-playing AI, there exists no standardised framework for reporting the obtained observations. As a result, it re…

cs.NE2020★ 82 cited

Benchmarking in Optimization: Best Practice and Open Issues

Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…

cs.NE2020★ 6 cited

Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems

Koen van der Blom, Timo M. Deist, Tea Tušar +5

Benchmarks are a useful tool for empirical performance comparisons. However, one of the main shortcomings of existing benchmarks is that it remains largely unclear how they relate…

cs.NE2020★ 1 cited

Expected Improvement versus Predicted Value in Surrogate-Based Optimization

Frederik Rehbach, Martin Zaefferer, Boris Naujoks +1

Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choi…