Visualisation with treemaps and sunbursts in many-objective optimisation

Abstract

Visualisation is an important aspect of evolutionary computation, enabling practitioners to explore the operation of their algorithms in an intuitive way and providing a better means for displaying their results to problem owners. The presentation of the complex data arising in many-objective evolutionary algorithms remains a challenge, and this work examines the use of treemaps and sunbursts for visualising such data. We present a novel algorithm for arranging a treemap so that it explicitly displays the dominance relations that characterise many-objective populations, as well as considering approaches for creating trees with which to represent multi- and many-objective solutions. We show that treemaps and sunbursts can be used to display important aspects of evolutionary computation, such as the diversity and convergence of a search population, and demonstrate the approaches on a range of test problems and a real-world problem from the literature.

The intersection of evolutionary computation and explainable AI

“…While there have been some usability studies relating to visualisation within EC (e.g. [43,67]), further efforts towards explanations through visualisation are possible. For instance, a usability study might be constructed that evaluates the accessibility of a proposed explanation, and gauges the extent to which it explains the aspect of EC that it seeks to.…”