Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms
Duc-Cuong Dang, Per Kristian Lehre · 2015
Population-based EAs can optimise pseudo-Boolean functions in expected polynomial time, even when only partial information about the problem is available [7]. In this paper, we show that the approach used to analyse optimisation with partial information extends naturally to optimisation under noise. We consider pseudo-Boolean problems with an additive noise term. Very general conditions on the noise term is derived, under which the EA optimises the noisy function in expected polynomial time. In the case of the Onemax and Leadingones problems, efficient optimisation is even possible when the variance of the noise distribution grows quickly with the problem size.