Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/128926
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Type: Conference paper
Title: Runtime analysis of evolutionary multi-objective algorithms optimising the degree and diameter of spanning trees
Author: Gao, W.
Pourhassan, M.
Roostapour, V.
Neumann, F.
Citation: Lecture Notes in Artificial Intelligence, 2019 / Deb, K., Goodman, E.D., Coello, C.A.C., Klamroth, K., Miettinen, K., Mostaghim, S., Reed, P.M. (ed./s), vol.11411, pp.504-515
Publisher: Springer
Publisher Place: Cham, Switzerland
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science; 11411
ISBN: 3030125971
9783030125974
ISSN: 0302-9743
1611-3349
Conference Name: 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO) (10 Mar 2019 - 13 Mar 2019 : East Lansing, USA)
Editor: Deb, K.
Goodman, E.D.
Coello, C.A.C.
Klamroth, K.
Miettinen, K.
Mostaghim, S.
Reed, P.M.
Statement of
Responsibility: 
Wanru Gao, Mojgan Pourhassan, Vahid Roostapour, and Frank Neumann
Abstract: Motivated by the telecommunication network design, we study the problem of finding diverse set of minimum spanning trees of a certain complete graph based on the two features which are maximum degree and diameter. In this study, we examine a simple multi-objective EA, GSEMO, in solving the two problems where we maximise or minimise the two features at the same time.With a rigorous runtime analysis, we provide understanding of how GSEMO optimize the set of minimum spanning trees in these two different feature spaces.
Keywords: Evolutionary multi-objective optimisation; Algorithm analysis
Rights: © Springer Nature Switzerland AG 2019
DOI: 10.1007/978-3-030-12598-1_40
Grant ID: http://purl.org/au-research/grants/arc/DP160102401
Published version: https://link.springer.com/book/10.1007/978-3-030-12598-1
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Computer Science publications

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