Journal of Biomedical Informatics
Optimizing customer's selection for configurable product in B2C e-commerce application
Computers in Industry
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
An intelligent WiMAX mobile network handoff mechanism with GPS consideration
Mobility '08 Proceedings of the International Conference on Mobile Technology, Applications, and Systems
Combinatorial optimization in system configuration design
Automation and Remote Control
Information Sciences: an International Journal
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Searching spatial configurations is a particular case of maximal constraint satisfaction problems, where constraints expressed by spatial and nonspatial properties guide the search process. In the spatial domain, binary spatial relations are typically used for specifying constraints while searching spatial configurations. Searching configurations is particularly intractable when configurations are derived from a combination of objects, which involves a hard combinatorial problem. This paper presents a genetic algorithm (GA) that combines a direct and an indirect approach to treating binary constraints in genetic operators. A new genetic operator combines randomness and heuristics for guiding the reproduction of new individuals in a population. Individuals are composed of spatial objects whose relationships are indexed by a content measure. This paper describes the GA and presents experimental results that compare the genetic versus a deterministic and a local-search algorithm. These experiments show the convenience of using a GA when the complexity of the queries and databases do no guarantee the tractability of a deterministic strategy.