3 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
Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to deal
with noise. By the sampling strategy, the fitness of a solution is evaluated
multiple times (called \emph{sample size}) independently, and its true fitness
is then approximated by the average of these evaluations. Previous studies on
sampling are mainly empirical. In this paper, we first investigate the effect
of sample size from a theoretical perspective. By analyzing the (1+1)-EA on the
noisy LeadingOnes problem, we show that as the sample size increases, the
running time can reduce from exponential to polynomial, but then return to
exponential. This suggests that a proper sample size is crucial in practice.
Then, we investigate what strategies can work when sampling with any fixed
sample size fails. By two illustrative examples, we prove that using parent or
offspring populations can be better. Finally, we construct an artificial noisy
example to show that when using neither sampling nor populations is effective,
adaptive sampling (i.e., sampling with an adaptive sample size) can work. This,
for the first time, provides a theoretical support for the use of adaptive
sampling
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful real-world
applications, where the optimization problems are often subject to a wide range
of uncertainties. To understand the practical behaviors of EAs theoretically,
there are a series of efforts devoted to analyzing the running time of EAs for
optimization under uncertainties. Existing studies mainly focus on noisy and
dynamic optimization, while another common type of uncertain optimization,
i.e., robust optimization, has been rarely touched. In this paper, we analyze
the expected running time of the (1+1)-EA solving robust linear optimization
problems (i.e., linear problems under robust scenarios) with a cardinality
constraint . Two common robust scenarios, i.e., deletion-robust and
worst-case, are considered. Particularly, we derive tight ranges of the robust
parameter or budget allowing the (1+1)-EA to find an optimal solution
in polynomial running time, which disclose the potential of EAs for robust
optimization.Comment: 17 pages, 1 tabl