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
