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| Content Provider | ACM Digital Library |
|---|---|
| Author | Barreto, André M.S. Bernardino, Heder S. Barbosa, Helio J.C. |
| Abstract | One of the many difficulties that arise in the empirical evaluation of new computational techniques is the analysis and reporting of experiments involving a large number of test-problems and algorithms. The performance profiles are a methodology specifically developed for this purpose which provides a simple means of visualizing and interpreting the results of large-scale benchmarking experiments. However good, performance profiles do not take into account the uncertainty present in most experimental settings. This paper presents an extension of this analytic tool called probabilistic performance profiles. The basic idea is to endow the original performance profiles with a probabilistic interpretation, which makes it possible to represent the expected performance of a stochastic algorithm in a convenient way. The benefits of the new method are demonstrated with data from a real benchmark experiment involving several problems and algorithms. |
| Starting Page | 751 |
| Ending Page | 758 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450300728 |
| DOI | 10.1145/1830483.1830617 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Performance profiles Experimental evaluation |
| Content Type | Text |
| Resource Type | Article |
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