| Title: | Generalization ability of feature-based performance prediction models : a statistical analysis across benchmarks |
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| Authors: | ID Nikolikj, Ana, Institut Jožef Stefan (Author)
ID Kostovska, Ana, Institut Jožef Stefan (Author)
ID Cenikj, Gjorgjina, Institut Jožef Stefan (Author)
ID Doerr, Carola (Author)
ID Eftimov, Tome, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://ieeexplore.ieee.org/document/10611952
 PDF - Presentation file, download (1,29 MB)
MD5: CFAE608E802112036A4DFAC6B41B8FAB
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| Language: | English |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | This study examines the generalization ability of algorithm performance prediction models across various bench-mark 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. |
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| Keywords: | meta-learning, single-objective optimization, module importance |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 08.08.2024 |
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| Publisher: | IEEE |
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| Year of publishing: | 2024 |
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| Number of pages: | 8 str. |
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| Source: | ZDA |
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| PID: | 20.500.12556/DiRROS-20434  |
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| UDC: | 004.85 |
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| DOI: | 10.1109/CEC60901.2024.10611952 |
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| COBISS.SI-ID: | 206945027 |
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| Copyright: | © 2024 IEEE |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 10. 9. 2024;
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| Publication date in DiRROS: | 16.09.2024 |
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| Views: | 11 |
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| Downloads: | 7 |
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