تقرير
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
| العنوان: | Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks |
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| المؤلفون: | Nikolikj, Ana, Kostovska, Ana, Cenikj, Gjorgjina, Doerr, Carola, Eftimov, Tome |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing |
| الوصف: | This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances. Comment: To appear in the Proc. of the 2024 IEEE World Congress on Computational - Congress on Evolutionary Computation |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2405.12259Test |
| رقم الانضمام: | edsarx.2405.12259 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |