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Visualisation with treemaps and sunbursts in many-objective optimisation

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 evolut...

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Detalles Bibliográficos
Autor principal: Walker, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417388/
https://www.ncbi.nlm.nih.gov/pubmed/30956541
http://dx.doi.org/10.1007/s10710-018-9329-0
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author Walker, David J.
author_facet Walker, David J.
author_sort Walker, David J.
collection PubMed
description 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.
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spelling pubmed-64173882019-04-03 Visualisation with treemaps and sunbursts in many-objective optimisation Walker, David J. Genet Program Evolvable Mach Article 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. Springer US 2018-08-07 2018 /pmc/articles/PMC6417388/ /pubmed/30956541 http://dx.doi.org/10.1007/s10710-018-9329-0 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Walker, David J.
Visualisation with treemaps and sunbursts in many-objective optimisation
title Visualisation with treemaps and sunbursts in many-objective optimisation
title_full Visualisation with treemaps and sunbursts in many-objective optimisation
title_fullStr Visualisation with treemaps and sunbursts in many-objective optimisation
title_full_unstemmed Visualisation with treemaps and sunbursts in many-objective optimisation
title_short Visualisation with treemaps and sunbursts in many-objective optimisation
title_sort visualisation with treemaps and sunbursts in many-objective optimisation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417388/
https://www.ncbi.nlm.nih.gov/pubmed/30956541
http://dx.doi.org/10.1007/s10710-018-9329-0
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