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arXiv · 2006.12309

Visualising Evolution History in Multi- and Many-Objective Optimisation

Mathew Walter·David Walker·Matthew Craven
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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.

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Mathew Walter, David Walker, Matthew Craven. 2020-06-22. Visualising Evolution History in Multi- and Many-Objective Optimisation. https://arxiv.org/abs/2006.12309

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