  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.
