Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

0494112 - ÚI 2019 RIV DE eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 72-79. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
R&D Projects: GA ČR GA17-01251S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Gradient boosting * Random forest * Black-box optimization * Surrogate model * Benchmarking
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/72.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0287361