1 research outputs found
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
In real-world optimization tasks, the objective (i.e., fitness) function
evaluation is often disturbed by noise due to a wide range of uncertainties.
Evolutionary algorithms (EAs) have been widely applied to tackle noisy
optimization, where reducing the negative effect of noise is a crucial issue.
One popular strategy to cope with noise is sampling, which evaluates the
fitness multiple times and uses the sample average to approximate the true
fitness. In this paper, we introduce median sampling as a noise handling
strategy into EAs, which uses the median of the multiple evaluations to
approximate the true fitness instead of the mean. We theoretically show that
median sampling can reduce the expected running time of EAs from exponential to
polynomial by considering the (1+1)-EA on OneMax under the commonly used
one-bit noise. We also compare mean sampling with median sampling by
considering two specific noise models, suggesting that when the 2-quantile of
the noisy fitness increases with the true fitness, median sampling can be a
better choice. The results provide us with some guidance to employ median
sampling efficiently in practice.Comment: 19 pages. arXiv admin note: text overlap with arXiv:1810.05045,
arXiv:1711.0095