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
Accepted author manuscript, 524 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
| Publication date | 6/07/2018 |
|---|---|
| Host publication | GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 31-32 |
| Number of pages | 2 |
| ISBN (electronic) | 9781450357647 |
| <mark>Original language</mark> | English |
| Event | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15/07/2018 → 19/07/2018 |
| Conference | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 |
|---|---|
| Country/Territory | Japan |
| City | Kyoto |
| Period | 15/07/18 → 19/07/18 |
| Conference | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 |
|---|---|
| Country/Territory | Japan |
| City | Kyoto |
| Period | 15/07/18 → 19/07/18 |
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.