COCO: Performance Assessment
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Abstract (arXiv)
We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform. The performance assessment is based on runtimes measured in number of objective function evaluations to reach one or several quality indicator target values. We argue that runtime is the only available measure with a generic, meaningful, and quantitative interpretation. We discuss the choice of the target values, runlength-based targets, and the aggregation of results by using simulated restarts, averages, and empirical distribution functions.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2016.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 1; 2026: 1.
7 papers.
- 2024Understanding Impacts of Electromagnetic Signal Injection Attacks on Object Detectioncited by 5
- 2024A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problemscited by 5
- 2024AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognitioncited by 2
- 2025MicroNAS: An Automated Framework for Developing a Fall Detection Systemcited by 1
- 2022Trial-Based Dominance Enables Non-Parametric Tests to Compare both the Speed and Accuracy of Stochastic Optimizerscited by 0
- 2024Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysiscited by 0
- 2026Geometric Probing for Algorithm Selection in Continuous Black-Box Optimizationcited by 0
- 2026Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization10 Apr 2026
- 2025MicroNAS: An Automated Framework for Developing a Fall Detection System10 Apr 2025
- 2024A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems4 Oct 2024
- 2024Understanding Impacts of Electromagnetic Signal Injection Attacks on Object Detection23 Jul 2024
- 2024AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition4 May 2024
- 2024Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis11 Mar 2024
- 2022Trial-Based Dominance Enables Non-Parametric Tests to Compare both the Speed and Accuracy of Stochastic Optimizers19 Dec 2022