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16th FOGA 2021: Virtual Event, Austria

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Steffen Finck, Michael Hellwig, Pietro S. Oliveto:
FOGA '21: Foundations of Genetic Algorithms XVI, Virtual Event, Austria, September 6-8, 2021. ACM 2021, ISBN 978-1-4503-8352-3
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  - conf/foga/MeunierLCT21
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Laurent Meunier, Iskander Legheraba, Yann Chevaleyre, Olivier Teytaud:
Asymptotic convergence rates for averaging strategies. 1:1-1:11
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  - conf/foga/Witt21
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Carsten Witt:
On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms. 2:1-2:15
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  - conf/foga/BransonS21
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Luke Branson, Andrew M. Sutton:
Focused jump-and-repair constraint handling for fixed-parameter tractable graph problems. 3:1-3:10
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  - conf/foga/CorusOY21
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Dogan Corus, Pietro S. Oliveto, Donya Yazdani:
Automatic adaptation of hypermutation rates for multimodal optimisation. 4:1-4:12
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  - conf/foga/FajardoS21
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Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. 5:1-5:15
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  - conf/foga/MullerG21
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Nils Müller, Tobias Glasmachers:
Non-local optimization: imposing structure on optimization problems by relaxation. 6:1-6:10
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  - conf/foga/HeinsBPSTK21
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Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, Pascal Kerschke:
On the potential of normalized TSP features for automated algorithm selection. 7:1-7:15
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  - conf/foga/BossekS21
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Jakob Bossek, Dirk Sudholt:
Do additional optima speed up evolutionary algorithms? 8:1-8:11
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  - conf/foga/NikfarjamB0N21
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Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann:
Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation. 9:1-9:11
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  - conf/foga/AntipovN21
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Denis Antipov, Semen Naumov:
The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions. 10:1-10:15

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