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research
Visualising many-objective populations
Authors
Richard M. Everson
Jonathan E. Fieldsend
David J. Walker
Publication date
1 January 2012
Publisher
'Association for Computing Machinery (ACM)'
Doi
Cite
Abstract
Copyright © 2012 ACM14th International Conference on Genetic and Evolutionary Computation (GECCO 2012), Philadelphia, USA, 7-11 July 2012Optimisation problems often comprise a large set of objectives, and visualising the set of solutions to a problem can help with understanding them, assisting a decision maker. If the set of objectives is larger than three, visualising solutions to the problem is a difficult task. Techniques for visualising high-dimensional data are often difficult to interpret. Conversely, discarding objectives so that the solutions can be visualised in two or three spatial dimensions results in a loss of potentially important information. We demonstrate four methods for visualising many-objective populations, two of which use the complete set of objectives to present solutions in a clear and intuitive fashion and two that compress the objectives of a population into two dimensions whilst minimising the information that is lost. All of the techniques are illustrated on populations of solutions to optimisation test problems
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Open Research Exeter
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oai:ore.exeter.ac.uk:10871/117...
Last time updated on 06/08/2013
Crossref
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info:doi/10.1145%2F2330784.233...
Last time updated on 04/12/2019