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Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm
Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia; Department of Electrical Engineering and Automation, Aalto University, Finland.
Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia; Department of Electrical Engineering and Automation, Aalto University, Finland.ORCID iD: 0000-0002-9315-9920
2019 (English)In: Proceedings: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2019, p. 1265-1268Conference paper, Published paper (Refereed)
Abstract [en]

Search-based software engineering aims to apply different search-based techniques to software engineering problems. Automation of software development is one such problem. In this paper we evaluate the permutation-based individual encoding for automatic reconstruction of measurement connections in a closed-loop control system using evolutionary algorithm and model checking. Using the permutation-based encoding greatly increases the difficulty of the considered problem, but makes it much closer to the real world scenarios. The results show that even the simple (1+1) evolutionary algorithm can successfully solve the realistic optimization problem with large search space size, although it struggles to find the optimal solution within reasonable time on the hardest problem instance.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1265-1268
Series
International Conference on Emerging Technologies and Factory Automation (ETFA), E-ISSN 1946-0759
Keywords [en]
search-based software engineering, evolutionary computation, model checking, automatic model synthesis
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-85950DOI: 10.1109/ETFA.2019.8869114ISI: 000556596600169Scopus ID: 2-s2.0-85074202348OAI: oai:DiVA.org:ltu-85950DiVA, id: diva2:1572276
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2019), Zaragoza, Spain, September 10-13, 2019
Note

ISBN för värdpublikation: 978-1-7281-0303-7;

Finansiär: Government of the Russian Federation (08-08)

Available from: 2021-06-23 Created: 2021-06-23 Last updated: 2024-03-07Bibliographically approved

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Vyatkin, Valeriy

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