Počet záznamů: 1
Knowledge-based Selection of Gaussian Process Surrogates
- 1.
SYSNO ASEP 0509320 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Knowledge-based Selection of Gaussian Process Surrogates Tvůrce(i) Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. IAL ECML PKDD 2019: Workshop & Tutorial on Interactive Adaptive Learning. Proceedings. - Aachen : Technical University & CreateSpace Independent Publishing Platform, 2019 / Kottke D. ; Lemaire D. ; Calma A. ; Krempl G. ; Holzinger A. - ISSN 1613-0073 Rozsah stran s. 48-63 Poč.str. 16 s. Forma vydání Online - E Akce ECML PKDD 2019: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Datum konání 16.09.2019 - 20.09.2019 Místo konání Würzburg Země DE - Německo Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova Benchmarking ; Black-box optimization ; Gaussian process ; Landscape analysis Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR GA18-18080S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85072731524 Anotace Many real-world problems belong to the area of continuous black-box optimization. If the black-box function is also cost-aware, regression surrogate models are often utilized by optimization algorithms to save evaluations of the original cost-aware function. Choosing a suitable surrogate model or a suitable setting of its hyperparameters is a complex selection problem, where research into reusing knowledge represented by features of black-box function landscape is only starting. In this paper, we report the research into surrogate model selection, where knowledge from the previous experience with using the model is utilized to design a metalearing system. As a proof of concept, we provide a study investigating the influence of landscape features on the performance of various Gaussian process covariance functions as surrogate models for the state-of-the-art optimization algorithm in the cost-aware continuous black-box optimization. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://ceur-ws.org/Vol-2444/ialatecml_paper4.pdf
Počet záznamů: 1