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Generating approximate region boundaries from heterogeneous spatial information: an evolutionary approach

Schockaert, Steven ORCID:, Smart, Philip David and Twaroch, Florian 2011. Generating approximate region boundaries from heterogeneous spatial information: an evolutionary approach. Information Sciences 181 (2) , pp. 257-283. 10.1016/j.ins.2010.09.021

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Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
University IT
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Uncontrolled Keywords: Spatial reasoning ; Genetic algorithms ; Ant colony ; optimization ; Geographic information retrieval
Publisher: Elsevier
ISSN: 0020-0255
Last Modified: 29 Sep 2023 01:07

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