Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/136935
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Type: Conference paper
Title: Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
Author: Neumann, F.
Witt, C.
Citation: Lecture Notes in Artificial Intelligence, 2022 / Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tusar, T. (ed./s), vol.13399, pp.542-554
Publisher: Springer
Publisher Place: Cham, Switzerland
Issue Date: 2022
Series/Report no.: Lecture Notes in Computer Science; 13399
ISBN: 9783031147203
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Parallel Problem Solving from Nature (PPSN) (10 Sep 2022 - 14 Sep 2022 : Dortmund, Germany)
Editor: Rudolph, G.
Kononova, A.V.
Aguirre, H.
Kerschke, P.
Ochoa, G.
Tusar, T.
Statement of
Responsibility: 
Frank Neumann and Carsten Witt
Abstract: Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods. Motivated by studies on separable functions and the optimization behaviour of evolutionary algorithms as well as objective functions from the area of chance constrained optimization, we study the class of objective functions that are weighted sums of two transformed linear functions. Our results show that the (1+1) EA, with a mutation rate depending on the number of overlapping bits of the functions, obtains an optimal solution for these functions in expected time O(n log n), thereby generalizing a well-known result for linear functions to a much wider range of problems.
Rights: © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
DOI: 10.1007/978-3-031-14721-0_38
Grant ID: http://purl.org/au-research/grants/arc/FT200100536
Published version: https://link.springer.com/book/10.1007/978-3-031-14721-0
Appears in Collections:Computer Science publications

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