Rights statement: © The owners/authors, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion (2018) http://doi.acm.org/10.1145/3205651.3208208
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Summary of evolutionary computation for wind farm layout optimization
AU - Wilson, Dennis G.
AU - Rodrigues, Silvio
AU - Segura, Carlos
AU - Loshchilov, Ilya
AU - Huer, Frank
AU - Buenl, Guillermo López
AU - Kheiri, Ahmed
AU - Keedwell, Ed
AU - Ocampo-Pineda, Mario
AU - Özcan, Ender
AU - Pea, Sergio Ivvan Valdez
AU - Goldman, Brian
AU - Rionda, Salvador Botello
AU - Hernndez-Aguirre, Arturo
AU - Veeramachaneni, Kalyan
AU - Cussat-Blanc, Sylvain
N1 - © The owners/authors, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion (2018) http://doi.acm.org/10.1145/3205651.3208208
PY - 2018/7/6
Y1 - 2018/7/6
N2 - This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of ve generated wind farms based on a sim-plied cost of energy evaluation function of the wind farm layouts. Online and oine APIs were implemented in C++, Java, Matlab and Python for this competition to oer a common framework for the competitors. e top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation.
AB - This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of ve generated wind farms based on a sim-plied cost of energy evaluation function of the wind farm layouts. Online and oine APIs were implemented in C++, Java, Matlab and Python for this competition to oer a common framework for the competitors. e top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation.
KW - Real-world problem
KW - Wind farm layout optimization
U2 - 10.1145/3205651.3208208
DO - 10.1145/3205651.3208208
M3 - Conference contribution/Paper
AN - SCOPUS:85051557430
SP - 31
EP - 32
BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -