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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Vydané v:Genetic programming and evolvable machines Ročník 19; číslo 3; s. 421 - 452
Hlavný autor: Walker, David J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 2018
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Electrical Engineering
Evolutionary algorithms
Evolutionary computation
Evolutionary Computation and Visualization
Genetic Programming
Interpreters
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Visualization
Visualisation
Many-objective optimisation
Evolutionary computation
ISSN:1389-2576, 1573-7632
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Shrnutí: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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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-018-9329-0