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dc.contributor.author
Lengler, Johannes
dc.contributor.author
Meier, Jonas
dc.contributor.editor
Bäck, Thomas
dc.contributor.editor
Preuss, Mike
dc.contributor.editor
Deutz, André
dc.contributor.editor
Wang, Hao
dc.contributor.editor
Doerr, Carola
dc.contributor.editor
Emmerich, Michael
dc.contributor.editor
Trautmann, Heike
dc.date.accessioned
2021-01-15T10:13:59Z
dc.date.available
2021-01-14T15:28:29Z
dc.date.available
2021-01-15T10:13:59Z
dc.date.issued
2020
dc.identifier.isbn
978-3-030-58111-4
en_US
dc.identifier.isbn
978-3-030-58112-1
en_US
dc.identifier.issn
0302-9743
dc.identifier.issn
1611-3349
dc.identifier.other
10.1007/978-3-030-58112-1_42
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/462680
dc.description.abstract
Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the (1+1)-Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for Dynamic BinVal, the extremal form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for (μ+1)-EA is not close the optimum, but far away from it. In contrast, for the (μ+1)-GA, the region around the optimum is the hardest region in all studied cases (Extended Abstract. A full version is available on arxiv at [11]).
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.title
Large Population Sizes and Crossover Help in Dynamic Environments
en_US
dc.type
Conference Paper
dc.date.published
2020-08-31
ethz.book.title
Parallel Problem Solving from Nature – PPSN XVI
en_US
ethz.journal.title
Lecture Notes in Computer Science
ethz.journal.volume
12269
en_US
ethz.journal.abbreviated
LNCS
ethz.pages.start
610
en_US
ethz.pages.end
622
en_US
ethz.event
16th International Conference on Parallel Problem Solving from Nature (PPSN 2020)
en_US
ethz.event.location
Leiden, Netherlands
ethz.event.date
September 5-9, 2020
en_US
ethz.publication.place
Cham
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03672 - Steger, Angelika / Steger, Angelika
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03672 - Steger, Angelika / Steger, Angelika
ethz.date.deposited
2021-01-14T15:28:41Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-15T10:14:07Z
ethz.rosetta.lastUpdated
2024-02-02T12:53:08Z
ethz.rosetta.versionExported
true
ethz.COinS
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