3,077 research outputs found
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
Bounded perturbation resilience of extragradient-type methods and their applications
In this paper we study the bounded perturbation resilience of the
extragradient and the subgradient extragradient methods for solving variational
inequality (VI) problem in real Hilbert spaces. This is an important property
of algorithms which guarantees the convergence of the scheme under summable
errors, meaning that an inexact version of the methods can also be considered.
Moreover, once an algorithm is proved to be bounded perturbation resilience,
superiorizion can be used, and this allows flexibility in choosing the bounded
perturbations in order to obtain a superior solution, as well explained in the
paper. We also discuss some inertial extragradient methods. Under mild and
standard assumptions of monotonicity and Lipschitz continuity of the VI's
associated mapping, convergence of the perturbed extragradient and subgradient
extragradient methods is proved. In addition we show that the perturbed
algorithms converges at the rate of . Numerical illustrations are given
to demonstrate the performances of the algorithms.Comment: Accepted for publication in The Journal of Inequalities and
Applications. arXiv admin note: text overlap with arXiv:1711.01936 and text
overlap with arXiv:1507.07302 by other author
Forget metamaterial: It does not improve sound absorption performance as it claims
The term `sub-wavelength' is commonly used to describe innovative
sound-absorbing structures usually labeled as `metamaterials'. Such structures,
however, inherently do not bring groundbreaking advancements. This study
addresses the limitations imposed by the thickness criterion of Yang et al. by
introducing the concept of equivalent mass-spring-damping parameters within the
resonator framework. This innovative approach introduces an index of
`half-absorption bandwidth' to effectively overcome the thickness restriction.
Four practical cases are then presented to correct prevalent misleading
conceptions about low-frequency, broadband absorption as claimed. The
phenomenon of mass disappearing in the expression of sound absorption
coefficient supports the conclusion that volume is the only determinant factor
in sound absorption performance. Any attempts to improve sound absorption
solely through geometry and structural designs would inevitably sacrifice the
half-absorption bandwidth. Additionally, the concept of negative stiffness or
bulk modulus is merely a mathematical convention without any real improvement
in absorption performance. Overall, this research focuses on the physical
mechanism of sound-absorbing structures by correcting traditional
misunderstandings, and offers a comprehensive framework for assessing and
enhancing sound absorption.Comment: 12 pages, 5 figures, part of the first author's Ph.D. thesi
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