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From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications
ITMO University, Saint-Petersburg, Russia.
ITMO University, Saint-Petersburg, Russia.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. ITMO University, Saint-Petersburg, Russia and Aalto University, Helsinki, Finland.ORCID iD: 0000-0002-9315-9920
2018 (English)In: GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York: ACM Digital Library, 2018, p. 1902-1905Conference paper, Published paper (Refereed)
Abstract [en]

Search-based software engineering, a discipline that often requires finding optimal solutions, can be a viable source for problems that bridge theory and practice of evolutionary computation. In this research we consider one such problem: generation of data connections in a distributed control application designed according to the IEC 61499 industry standard.

We perform the analysis of the fitness landscape of this problem and find why exactly the simplistic (1 + 1) evolutionary algorithm is slower than expected when finding an optimal solution to this problem. To counteract, we develop a population-based algorithm that explicitly maximises diversity among the individuals in the population. We show that this measure indeed helps to improve the running times

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2018. p. 1902-1905
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-70150DOI: 10.1145/3205651.3208230ISBN: 978-1-4503-5764-7 (electronic)OAI: oai:DiVA.org:ltu-70150DiVA, id: diva2:1234171
Conference
GECCO 18 Kyoto, Japan, July 15-19, 2018
Available from: 2018-07-23 Created: 2018-07-23 Last updated: 2018-08-14Bibliographically approved

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Vyatkin, Valeriy

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