Login / Signup

Visualisation with treemaps and sunbursts in many-objective optimisation.

David J Walker
Published in: Genetic programming and evolvable machines (2018)
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.
Keyphrases
  • machine learning
  • deep learning
  • genome wide
  • electronic health record
  • big data
  • systematic review
  • primary care
  • mental health
  • case report
  • data analysis
  • genetic diversity