Visual Design Space Exploration using Contextual Self-Organizing Maps

Self-organizing maps (SOMs) and contextual maps are methods of visualizing high dimensional data in a low dimensional space. SOMs have previously been applied to visualize characteristics of optimization problems by generating maps of the component variables to compare interactions and relationships between design variables. In this paper, SOMs and contextual maps are explored as a visualization method to directly visualize the design space. Using the techniques described in the paper, high dimensional datasets are reduced to a 2D, human readable, visual map. Preliminary results show that the topology of three optimization functions using varying dimensionality can be clustered and visualized using contextual maps, and information can be gathered from these clusters including objective function values and variability amongst differing areas of the design space. This paper focuses on the use of contextual maps to extract valuable information such as modality and curvature to aid in future work such as selection of appropriate optimization algorithm and initial point for a solution run.