Automated design of efficient swarming behaviours: a Q-learning hyper-heuristic approach

Reference : Automated design of efficient swarming behaviours: a Q-learning hyper-heuristic approach


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/43999

Title :	Automated design of efficient swarming behaviours: a Q-learning hyper-heuristic approach
Language :	English
Author, co-author :	Duflo, Gabriel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Danoy, Grégoire [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)]
	Talbi, El-Ghazali [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) > ; University of Lille, INRIA, France]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Publication date :	2020
Main document title :	GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020
Publisher :	ACM
Pages :	227--228
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	Genetic and Evolutionary Computation Conference (GECCO 2020)
Event date :	from 01-07-2020 to 12-07-2020
Permalink :	http://hdl.handle.net/10993/43999
DOI :	10.1145/3377929.3390026
Other URL :	https://doi.org/10.1145/3377929.3390026

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