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Abstract

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III.

DOI

10.1007/978-3-030-58115-2_21

Publication Date

2020-05-27

Publication Title

Lecture Notes in Computer Science 12270

First Page

299

Last Page

312

ISSN

0302-9743

Embargo Period

2021-09-02

Organisational Unit

School of Engineering, Computing and Mathematics

Keywords

Visualisation, Evolutionary computation, Multi-objective optimisation

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