Evolutionary visual exploration: experimental analysis of algorithm behaviour.

Recent publications in the domains of interactive evolutionary computation and data visualisation consider an emerging topic coined Evolutionary Visual Exploration (EVE). EVE systems combine visual analytics with stochastic optimisation to aid the exploration of complex, multidimensional datasets. In this work we present an experimental analysis of the behaviour of an EVE system that is dedicated to the visualisation of multidimensional datasets, which are generally characterised by a large number of possible views or projections. EvoGraphDice is an interactive evolutionary system that progressively evolves a small set of new dimensions, to provide new viewpoints on the dataset, in the form of linear and non-linear combinations of the original dimensions. The criteria for evolving new dimensions are not known a priori and are partially specified by the user via an interactive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function to take into account the user input, and then calculates new views, with the help of an evolutionary engine. In previous work (an observational study), we showed that EvoGraphDice was able to facilitate "exploration" tasks, helping users to discover new interesting views and relationships in their data. Here, we focus on the system's "convergence" behavior, conducting an experiment with users who have a precise task to perform. The experimental task is set up as a geometrical game, and collected data show that EvoGraphDice is able to "learn" user preferences in a way that helps users fulfill their task (i.e. converge to desired solutions).