Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/101592
Title: An Evolutionary Approach to Passive Learning in Optimal Control Problems
Authors: Blueschke, D.
Savin, I.
Blueschke-Nikolaeva, V.
Issue Date: 2020
Publisher: Springer
Citation: Blueschke D. An Evolutionary Approach to Passive Learning in Optimal Control Problems / D. Blueschke, I. Savin, V. Blueschke-Nikolaeva. — DOI 10.1007/s10614-019-09961-4 // Computational Economics. — 2020. — Vol. 56. — Iss. 3. — P. 659-673.
Abstract: We consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317–338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research. © 2019, The Author(s).
Keywords: DIFFERENTIAL EVOLUTION
OPTIMAL CONTROL
PASSIVE LEARNING
STOCHASTIC PROBLEMS
URI: http://hdl.handle.net/10995/101592
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85077600937
PURE ID: 20127615
f0fd9e67-0382-497e-aa78-eac37d52ac47
ISSN: 9277099
DOI: 10.1007/s10614-019-09961-4
metadata.dc.description.sponsorship: Open access funding provided by University of Klagenfurt.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85077600937.pdf495,29 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.