Počet záznamů: 1
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy
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SYSNO ASEP 0494112 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy Tvůrce(i) Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Repický, Jakub (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. - Aachen : Technical University & CreateSpace Independent Publishing Platform, 2018 / Krajči S. - ISSN 1613-0073 Rozsah stran s. 72-79 Poč.str. 8 s. Forma vydání Online - E Akce ITAT 2018. Conference on Information Technologies – Applications and Theory /18./ Datum konání 21.09.2018 - 25.09.2018 Místo konání Plejsy Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova Gradient boosting ; Random forest ; Black-box optimization ; Surrogate model ; Benchmarking 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 Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85053828979 Anotace Many real-world problems belong to the area of continuous black-box optimization, where evolutionary optimizers have become very popular in spite of the fact that such optimizers require a great amount of real-world fitness function evaluations, which can be very expensive or time-consuming. Hence, regression surrogate models are often utilized to evaluate some points instead of the fitness function. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy (DTS-CMA-ES) is a surrogate-assisted version of the state-of-the-art continuous black-box optimizer CMA-ES using Gausssian processes as a surrogate model to predict the whole distribution of the fitness function. In this paper, the DTS-CMAES is studied in connection with the boosted regression forest, another regression model capable to estimate the distribution. Results of testing regression forest and Gaussian processes, the former in 20 different settings, as a surrogate models in the DTS-CMA-ES on the set of noiseless benchmarks are reported. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019 Elektronická adresa http://ceur-ws.org/Vol-2203/72.pdf
Počet záznamů: 1