EvoGraphDice: Interactive Evolution for Visual Analytics

Abstract : Visualization of large and complex datasets is a research challenge, especially in frameworks like industrial design, decision making and visual analytics. Interactive Evolution, used not only as an optimisation tool, but also as an exploration tool may provide some versatile solutions to this challenge. This paper presents an attempt in this direction with the EvoGraphDice prototype, developed on top of GraphDice, a general purpose visualization freeware for multidimensional visualization based on scatterplot matrices. EvoGraphDice interactively evolves compound additional dimensions, that provide new viewpoints on a multidimensional dataset. Compound dimensions are linear combination of the initial data dimensions, they are initialised with a Principal Component Analysis (PCA), and modified progressively by the interactive evolution process. Various interactions are available to the user, either in a transparent way, via a capture of mouse-clicks, or in a fully controlled manner, where the user has the opportunity to modify or include his own compound dimension in the evolved population, control the earch space, or do some interactive queries. EvoGraphDice is tested on a synthetic dataset of dimension 6, where a known dependency is rediscovered via interactive manipulation. A second example is presented, based on a real dataset of dimension 13, provided by an industrial partner. Our experiments prove the potential of this interactive approach, and allow us to sketch future directions of development for the EvoGraphDice prototype.
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Submitted on : Tuesday, July 31, 2012 - 12:31:08 PM
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Waldo Gonzalo Cancino Ticona, Nadia Boukhelifa, Evelyne Lutton. EvoGraphDice: Interactive Evolution for Visual Analytics. Proceedings of the 2012 IEEE Congress on Evolutionary Computation, Xiaodong Li, Jun 2012, Brisbane, Australia. pp.2286-2293, ⟨10.1109/CEC.2012.6256553 ⟩. ⟨hal-00721999⟩

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