Voronoi-Based Archive Sampling for Robust Optimisation
Doherty, K; Alyahya, K; Fieldsend, JE; et al.Akman, O
Date: 15 July 2018
Conference paper
Publisher
Association for Computing Machinery (ACM)
Publisher DOI
Abstract
We propose a framework for estimating the quality of solutions in a
robust optimisation setting by utilising samples from the search history
and using MC sampling to approximate a Voronoi tessellation.
This is used to determine a new point in the disturbance neighbourhood
of a given solution such that – along with the relevant
archived ...
We propose a framework for estimating the quality of solutions in a
robust optimisation setting by utilising samples from the search history
and using MC sampling to approximate a Voronoi tessellation.
This is used to determine a new point in the disturbance neighbourhood
of a given solution such that – along with the relevant
archived points – they form a well-spread distribution, and is also
used to weight the archive points to mitigate any selection bias in
the neighbourhood history. Our method performs comparably well
with existing frameworks when implemented inside a CMA-ES on
9 test problems collected from the literature in 2 and 10 dimensions.
Computer Science
Faculty of Environment, Science and Economy
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