Quantifying and optimizing visualization: An evolutionary computing-based approach

Advances in computing technology and computer graphics engulfed with huge collections of data have introduced new visualization techniques. This gives users many choices of visualization techniques to gain an insight about the dataset at hand. However, selecting the most suitable visualization for a given dataset and the task to be performed on the data is subjective. The work presented here introduces a set of visualization metrics to quantify visualization techniques. Based on a comprehensive literature survey, we propose effectiveness, expressiveness, readability, and interactivity as the visualization metrics. Using these metrics, a framework for optimizing the layout of a visualization technique is also presented. The framework is based on an evolutionary algorithm (EA) which uses treemaps as a case study. The EA starts with a randomly initialized population, where each chromosome of the population represents one complete treemap. Using the genetic operators and the proposed visualization metrics as an objective function, the EA finds the optimum visualization layout. The visualizations that evolved are compared with the state-of-the-art treemap visualization tool through a user study. The user study utilizes benchmark tasks for the evaluation. A comparison is also performed using direct assessment, where internal and external visualization metrics are used. Results are further verified using analysis of variance (ANOVA) test. The results suggest better performance of the proposed metrics and the EA-based framework for optimizing visualization layout. The proposed methodology can also be extended to other visualization techniques. (C) 2017 Elsevier Inc. All rights reserved.