ORCID
- Craven, Matthew: 0000-0001-9522-6173
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
2020-09-02
Publication Title
Lecture Notes in Computer Science 12270
ISSN
0302-9743
Embargo Period
2021-09-02
Organisational Unit
School of Engineering, Computing and Mathematics
Keywords
Visualisation, Evolutionary computation, Multi-objective optimisation
First Page
299
Last Page
312
Recommended Citation
Walter, M. J., Walker, D., & Craven, M. (2020) 'Visualising Evolution History in Multi- and Many-Objective Optimisation', Lecture Notes in Computer Science 12270, , pp. 299-312. Available at: https://doi.org/10.1007/978-3-030-58115-2_21
