Scalable Approximate Optimization of Objective Functions Represented by Random Forests-Reference-Cited by-同舟云学术

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Scalable Approximate Optimization of Objective Functions Represented by Random Forests

Published:2024-06-11 Issue: Volume:45 Page:494-499
ISSN:
Container-title:2024 32nd Mediterranean Conference on Control and Automation (MED)
language:
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Author:

Leonesio Marco¹,Fagiano Lorenzo²

Affiliation:

1. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - National Research Council of Italy (STIIMA - CNR),Milano,Italy,20133

2. Politecnico di Milano Piazza,Dipartimento di Elettronica, Informazione e Bioingegneria,Milano,Italy,20133

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10566119/10566120/10566222.pdf?arnumber=10566222

Reference18 articles.

1. SMGO: A set membership approach to data-driven global optimization

2. Reinforcement learning as data-driven optimization technique for GMAW process

3. Surrogate-Based Modeling and Optimization

4. Engineering Design via Surrogate Modelling

5. Surrogate-based analysis and optimization

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