| Naslov: | Generalization ability of feature-based performance prediction models : a statistical analysis across benchmarks |
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| Avtorji: | ID Nikolikj, Ana, Institut Jožef Stefan (Avtor)
ID Kostovska, Ana, Institut Jožef Stefan (Avtor)
ID Cenikj, Gjorgjina, Institut Jožef Stefan (Avtor)
ID Doerr, Carola (Avtor)
ID Eftimov, Tome, Institut Jožef Stefan (Avtor) |
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| Datoteke: |  URL - Izvorni URL, za dostop obiščite https://ieeexplore.ieee.org/document/10611952
 PDF - Predstavitvena datoteka, prenos (1,29 MB)
MD5: CFAE608E802112036A4DFAC6B41B8FAB
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| Jezik: | Angleški jezik |
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| Tipologija: | 1.08 - Objavljeni znanstveni prispevek na konferenci |
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| Organizacija: |  IJS - Institut Jožef Stefan
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| Povzetek: | 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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| Ključne besede: | meta-learning, single-objective optimization, module importance |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Recenzirani rokopis |
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| Datum objave: | 08.08.2024 |
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| Založnik: | IEEE |
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| Leto izida: | 2024 |
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| Št. strani: | 8 str. |
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| Izvor: | ZDA |
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| PID: | 20.500.12556/DiRROS-20434  |
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| UDK: | 004.85 |
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| DOI: | 10.1109/CEC60901.2024.10611952 |
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| COBISS.SI-ID: | 206945027 |
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| Avtorske pravice: | © 2024 IEEE |
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| Opomba: | Nasl. z nasl. zaslona;
Opis vira z dne 10. 9. 2024;
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| Datum objave v DiRROS: | 16.09.2024 |
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| Število ogledov: | 249 |
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| Število prenosov: | 110 |
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| Metapodatki: |    |
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