Understanding relationships amongst n-dimensional design spaces has long been a problem in the engineering community. Many visual methods previously developed, although useful, are limited to comparing three design variables at a time. Work described in this paper builds off the idea of a self-organizing map in order to visualize n-dimensional data on a two dimensional map. By using the contextual self-organizing map, current work shows that more design space information can be gleaned from map nodes themselves. By breaking the final visualization up into three maps containing separate contextual information, an investigator can quickly obtain information about the overall behavior of a design space. Tests run on well-known optimization functions show that information such as modality and curvature may be quickly suggested by these maps, and that they may provide enough information for a designer to choose a function to proceed with formal optimization of a given data set.

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