A feature rich distance-based many-objective visualisable test problem generator
Fieldsend, J., Chugh, T., Allmendinger, R., & Miettinen, K. (2019). A feature rich distance-based many-objective visualisable test problem generator. In GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference (pp. 541-549). ACM. https://doi.org/10.1145/3321707.3321727
Date
2019Discipline
TietotekniikkaLaskennallinen tiedeMultiobjective Optimization GroupMathematical Information TechnologyComputational ScienceMultiobjective Optimization GroupCopyright
© 2019 Association for Computing Machinery
In optimiser analysis and design it is informative to visualise how a
search point/population moves through the design space over time.
Visualisable distance-based many-objective optimisation problems
have been developed whose design space is in two-dimensions with
arbitrarily many objective dimensions. Previous work has shown
how disconnected Pareto sets may be formed, how problems can
be projected to and from arbitrarily many design dimensions, and
how dominance resistant regions of design space may be defined.
Most recently, a test suite has been proposed using distances to lines
rather than points. However, active use of visualisable problems has
been limited. This may be because the type of problem characteristics available has been relatively limited compared to many practical
problems (and non-visualisable problem suites). Here we introduce
the mechanisms required to embed several widely seen problem
characteristics in the existing problem framework. These include
variable density of solutions in objective space, landscape discontinuities, varying objective ranges, neutrality, and non-identical
disconnected Pareto set regions. Furthermore, we provide an automatic problem generator (as opposed to hand-tuned problem definitions). The flexibility of the problem generator is demonstrated
by analysing the performance of popular optimisers on a range of
sampled instances.
...
Publisher
ACMParent publication ISBN
978-1-4503-6111-8Conference
Genetic and Evolutionary Computation ConferenceIs part of publication
GECCO '19 : Proceedings of the Genetic and Evolutionary Computation ConferenceKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/30603930
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Additional information about funding
This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1] and the Natural Environment Research Council [grant number NE/P017436/1]. This research is related to the thematic research area DEMO (jyu.fi/demo) of the University of Jyväskylä.License
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