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CG Challenge
DOI: 10.4230/LIPIcs.SoCG.2020.84
Computing Low-Cost Convex Partitions for Planar Point Sets Based on a Memetic Approach (CG Challenge)

Authors: Laurent Moalic, Dominique Schmitt, Julien Lepagnot, and Julien Kritter

Published in: LIPIcs, Volume 164, 36th International Symposium on Computational Geometry (SoCG 2020)

Abstract
We present a memetic approach designed to tackle the 2020 Computational Geometry Challenge on the Minimum Convex Partition problem. It is based on a simple local search algorithm hybridized with a genetic approach. The population is brought down to its smallest possible size - only 2 individuals - for a very simple implementation. This algorithm was applied to all the instances, without any specific parameterization or adaptation.
Cite as

Laurent Moalic, Dominique Schmitt, Julien Lepagnot, and Julien Kritter. Computing Low-Cost Convex Partitions for Planar Point Sets Based on a Memetic Approach (CG Challenge). In 36th International Symposium on Computational Geometry (SoCG 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 164, pp. 84:1-84:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{moalic_et_al:LIPIcs.SoCG.2020.84,
  author =	{Moalic, Laurent and Schmitt, Dominique and Lepagnot, Julien and Kritter, Julien},
  title =	{{Computing Low-Cost Convex Partitions for Planar Point Sets Based on a Memetic Approach}},
  booktitle =	{36th International Symposium on Computational Geometry (SoCG 2020)},
  pages =	{84:1--84:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-143-6},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{164},
  editor =	{Cabello, Sergio and Chen, Danny Z.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2020.84},
  URN =		{urn:nbn:de:0030-drops-122423},
  doi =		{10.4230/LIPIcs.SoCG.2020.84},
  annote =	{Keywords: metaheuristics, memetic algorithms, convex partition optimization}
}
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