A genetic algorithm for automatic dashboard generation: first results

In this paper, we present a method for the automatic generation of dashboards (DBo) using a genetic algorithm (GA). A DBo is a set of visualizations, with possible linkage, intended to help users explore and analyse a dataset. Our Automatic Dashboard Generation System (ADGS) considers several input models for data, user and visualizations. We propose to represent a solution (i.e. a DBo) as a variable size matrix in which rows are visualizations and columns are data attributes. This representation can be evolved with a GA. For this purpose, we define genetic operators for DBo such as random generation, crossover, and mutation. We propose a fitness function to evaluate the quality of a DBo, as well as selection and replacement schemes. Finally, we present results with a benchmark dataset and a given user scenario. We show that the GA can find DBo that maximizes the evaluation function and that can be interesting for novice users. In perspectives, we will improve further the GA and we will study how to automatically propose a layout of the optimized DBo.