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Documents authored by Murray, Alan T.

Document
DOI: 10.4230/LIPIcs.GIScience.2023.4
Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Authors: Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
Cite as

Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen. Genetic Programming for Computationally Efficient Land Use Allocation Optimization. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{hildemann_et_al:LIPIcs.GIScience.2023.4,
  author =	{Hildemann, Moritz J. and Murray, Alan T. and Verstegen, Judith A.},
  title =	{{Genetic Programming for Computationally Efficient Land Use Allocation Optimization}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{4:1--4:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.4},
  URN =		{urn:nbn:de:0030-drops-188996},
  doi =		{10.4230/LIPIcs.GIScience.2023.4},
  annote =	{Keywords: Land use planning, Spatial optimization, Solution encoding, Computation time reduction}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.12
Heterogeneous Skeleton for Summarizing Continuously Distributed Demand in a Region

Authors: Alan T. Murray, Xin Feng, and Ali Shokoufandeh

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
There has long been interest in the skeleton of a spatial object in GIScience. The reasons for this are many, as it has proven to be an extremely useful summary and explanatory representation of complex objects. While much research has focused on issues of computational complexity and efficiency in extracting the skeletal and medial axis representations as well as interpreting the final product, little attention has been paid to fundamental assumptions about the underlying object. This paper discusses the implied assumption of homogeneity associated with methods for deriving a skeleton. Further, it is demonstrated that addressing heterogeneity complicates both the interpretation and identification of a meaningful skeleton. The heterogeneous skeleton is introduced and formalized, along with a method for its identification. Application results are presented to illustrate the heterogeneous skeleton and provides comparative contrast to homogeneity assumptions.
Cite as

Alan T. Murray, Xin Feng, and Ali Shokoufandeh. Heterogeneous Skeleton for Summarizing Continuously Distributed Demand in a Region. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 12:1-12:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

Copy BibTex To Clipboard

@InProceedings{murray_et_al:LIPIcs.GISCIENCE.2018.12,
  author =	{Murray, Alan T. and Feng, Xin and Shokoufandeh, Ali},
  title =	{{Heterogeneous Skeleton for Summarizing Continuously Distributed Demand in a Region}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{12:1--12:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.12},
  URN =		{urn:nbn:de:0030-drops-93400},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.12},
  annote =	{Keywords: Medial axis, Object center, Geographical summary, Spatial analytics}
}
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