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Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy
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SYSNO ASEP 0494112 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy Author(s) Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Repický, Jakub (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDSource Title 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 Pages s. 72-79 Number of pages 8 s. Publication form Online - E Action ITAT 2018. Conference on Information Technologies – Applications and Theory /18./ Event date 21.09.2018 - 25.09.2018 VEvent location Plejsy Country SK - Slovakia Event type EUR Language eng - English Country DE - Germany Keywords Gradient boosting ; Random forest ; Black-box optimization ; Surrogate model ; Benchmarking Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85053828979 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019 Electronic address http://ceur-ws.org/Vol-2203/72.pdf
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