Linear optimization for ecological indices based on aggregation functions
Version 2 2024-06-04, 03:29Version 2 2024-06-04, 03:29
Version 1 2016-08-31, 10:26Version 1 2016-08-31, 10:26
conference contribution
posted on 2024-06-04, 03:29 authored by Gleb BeliakovGleb Beliakov, A Geschke, Simon JamesSimon James, D NimmoWe consider an optimization problem in ecology where our objective is to maximize biodiversity with respect to different land-use allocations. As it turns out, the main problem can be framed as learning the weights of a weighted arithmetic mean where the objective is the geometric mean of its outputs. We propose methods for approximating solutions to this and similar problems, which are non-linear by nature, using linear and bilevel techniques.
History
Volume
611Pagination
411-422Location
Eindhoven, The NetherlandsPublisher DOI
Start date
2016-06-20End date
2016-06-24ISSN
1865-0929eISSN
1865-0937ISBN-13
9783319405803Language
engPublication classification
E Conference publication, E1 Full written paper - refereedCopyright notice
2016, Springer International Publishing SwitzerlandExtent
67Editor/Contributor(s)
Carvalho J, Lesot M, Kaymak U, Vieira S, Bouchon-Meunier B, Yager RTitle of proceedings
16th International Conference, IPMU 2016 Eindhoven, The Netherlands, June 20–24, 2016 Proceedings, Part IIEvent
Information processing and management of uncertainty in knowledge-based systems. Conference (16th : 2016 : Eindhoven, The Netherlands)Publisher
SpringerPlace of publication
Cham, SwitzerlandSeries
Communications in computer and information scienceUsage metrics
Categories
No categories selectedKeywords
Aggregation functionsLinear programmingWeight learningEcologyBiodiversitySchool of Information Technology970108 Expanding Knowledge in the Information and Computing Sciences080109 Pattern Recognition and Data Mining080503 Networking and Communications970101 Expanding Knowledge in the Mathematical Sciences4606 Distributed computing and systems software
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC