No abstract

No abstract

E cient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution's uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust tness approximations across the entire search history rather than a xed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.

No 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 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.