Codebase for EMO 2013 visualisation paper "Visualising high-dimensional Pareto relationships in two-dimensional scatterplots, Evolutionary Multi-criterion Optimization" by Jonathan E. Fieldsend and Richard M. Everson

Institutional paper repository:

https://ore.exeter.ac.uk/repository/handle/10871/11702

DOI:

http://dx.doi.org/10.1007/978-3-642-37140-0_42

Readme for release 1.0

Jonathan Fieldsend

This is research quality code, it undoubtedly could benefit from some refactoring, and there is some wasted computation, however I hope the comments (and the published work), make it relatively easy to follow. If you find a bug (and especially if you have a fix for it) please email the author at.

There are a number of supporting functions that have been sparsely commented (if commented at all), as they provide some processing which is shared across approaches; the three functions you will want to invoke directly are:

deterministic_compression_and_visualisation_closeness deterministic_compression_and_visualisation_dominance deterministic_compression_and_visualisation_koppenyoshida

Please use the help option for each of these methods.

The first two undertake the visualisations described in:

Fieldsend JE, Everson RM. Visualising high-dimensional Pareto relationships in two-dimensional scatterplots, Evolutionary Multi-criterion Optimisation, EMO 2013. LNCS pp 558-572

The last undertakes the visualisation described in

M. Koppen and K. Yoshida. Visualization of Pareto-sets in evolutionary multi-objective optimization. In Proceedings of the 7th International Conference on Hybrid Intelligent Systems, pages 156-161, Washington, DC, USA, 2007

given an already optimised permutation (Koppen and Yoshida used NSGA-II). If you have not provided a permutation for the non-dominated subset, then spectral seriation can be used instead (and is a built in option).