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.
Highlights
Visualisation remains an important topic within evolutionary computation and, as many-objective evolutionary algorithms (MaOEAs) continue to mature, the visualisation of solutions to many-objective problems is an important aspect of this [31]
Hierarchical information is common within evolutionary computation
This paper has presented treemaps and sunbursts for visualising data in evolutionary computation, focussing on populations of solutions to many-objective problems
Summary
Visualisation remains an important topic within evolutionary computation and, as many-objective evolutionary algorithms (MaOEAs) continue to mature, the visualisation of solutions to many-objective problems is an important aspect of this [31]. The treemaps presented therein were based on trees constructed in terms of dominance It is well known [16] that the dominance relation is poorly suited to comparing manyobjective solutions since, assuming an uniformly distributed objective space, the solutions are likely to be mutually non-dominating and incomparable. A new treemap layout algorithm is presented, designed to visualise many-objective populations with dominated solutions, and compared to an existing approach proposed by [26]. The well-known sunburst visualisation [40] is used to visualise many-objective populations; demonstrations show that they can be used to convey information about the optimisation characteristics (e.g., convergence and diversity) as well as the solution quality of a mutually non-dominating set.
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