Summary of Generalization Ability Of Feature-based Performance Prediction Models: a Statistical Analysis Across Benchmarks, by Ana Nikolikj et al.
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
by Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj, Carola Doerr, Tome Eftimov
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates how well algorithm performance prediction models generalize across various benchmark suites. The researchers compare the statistical similarity between different problem collections and the accuracy of models based on exploratory landscape analysis features. They find a positive correlation between these measures, indicating that when feature value distributions are similar, models tend to perform well. Two experiments validate these findings using standard benchmark suites and affine combinations of BBOB problem instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how good algorithm performance prediction models are at guessing how well they’ll do on different types of problems. The researchers compare the similarity between different groups of problems and the accuracy of models that use special features to analyze landscapes. They find that when the patterns in these landscapes are similar, the models tend to be good at guessing their own performance. Two tests check if this is true using common problem sets and mixes of BBOB problems. |
