The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
An Insight-Based Methodology for Evaluating Bioinformatics Visualizations
IEEE Transactions on Visualization and Computer Graphics
A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization
Analyzing active interactive genetic algorithms using visual analytics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evaluating Information Visualizations
Information Visualization
New Topics from Recent Interactive Evolutionary Computation Researches
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization
IEEE Transactions on Visualization and Computer Graphics
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Graphdice: a system for exploring multivariate social networks
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
The four-level nested model revisited: blocks and guidelines
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
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We present an Evolutionary Visual Exploration (EVE) system that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible views or projections. Starting from dimensions whose values are automatically calculated by a PCA, an interactive evolutionary algorithm progressively builds (or evolves) non-trivial viewpoints in the form of linear and non-linear dimension combinations, to help users discover new interesting views and relationships in their data. The criteria for evolving new dimensions is 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 (optimised by the evolutionary algorithm) to take into account the user input, and then calculates new views. Our method leverages automatic tools to detect interesting visual features and human interpretation to derive meaning, validate the findings and guide the exploration without having to grasp advanced statistical concepts. To validate our method, we built a prototype tool (EvoGraphDice) as an extension of an existing scatterplot matrix inspection tool, and conducted an observational study with five domain experts. Our results show that EvoGraphDice can help users quantify qualitative hypotheses and try out different scenarios to dynamically transform their data. Importantly, it allowed our experts to think laterally, better formulate their research questions and build new hypotheses for further investigation.