29,612 research outputs found
A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation
In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
This paper proposes a set of new error criteria and learning approaches,
Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex
optimization problem in training deep neural networks (DNNs). Theoretically, we
demonstrate its effectiveness on global and local convexity lower-bounded by
the standard -norm error. By analyzing the gradient on the convexity index
, we explain the reason why to learn adaptively using
gradient descent works. In practice, we show how this method improves training
of deep neural networks to solve visual recognition tasks on the MNIST and
CIFAR-10 datasets. Without using pretraining or other tricks, we obtain results
comparable or superior to those reported in recent literature on the same tasks
using standard ConvNets + MSE/cross entropy. Performance on deep/shallow
multilayer perceptrons and Denoised Auto-encoders is also explored. ANRAT can
be combined with other quasi-Newton training methods, innovative network
variants, regularization techniques and other specific tricks in DNNs. Other
than unsupervised pretraining, it provides a new perspective to address the
non-convex optimization problem in DNNs.Comment: AAAI 2016, 0.39%~0.4% ER on MNIST with single 32-32-256-10 ConvNets,
code available at https://github.com/cauchyturing/ANRA
Adaptive Image Denoising by Targeted Databases
We propose a data-dependent denoising procedure to restore noisy images.
Different from existing denoising algorithms which search for patches from
either the noisy image or a generic database, the new algorithm finds patches
from a database that contains only relevant patches. We formulate the denoising
problem as an optimal filter design problem and make two contributions. First,
we determine the basis function of the denoising filter by solving a group
sparsity minimization problem. The optimization formulation generalizes
existing denoising algorithms and offers systematic analysis of the
performance. Improvement methods are proposed to enhance the patch search
process. Second, we determine the spectral coefficients of the denoising filter
by considering a localized Bayesian prior. The localized prior leverages the
similarity of the targeted database, alleviates the intensive Bayesian
computation, and links the new method to the classical linear minimum mean
squared error estimation. We demonstrate applications of the proposed method in
a variety of scenarios, including text images, multiview images and face
images. Experimental results show the superiority of the new algorithm over
existing methods.Comment: 15 pages, 13 figures, 2 tables, journa
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Robust optimization of cardiovascular stents: A comparison of methods
This is the post-print version of the Article. The official published version can be accssed from the links below. Copyright @ 2004 Taylor & FrancisModern engineering design contains both creative and analytic components. This paper discusses the design process and illustrates links between design optimization and conceptual design through the re-design of a cardiovascular stent. A comparison is presented of two methods for design improvement: genetic algorithms (GA) and model-based robust engineering design (RED). Computational fluid dynamics (CFD) models are used to generate measurements of the quality of competing designs based on the concept of dissipated power. Alternative performance measures are also discussed. Environmental noise is introduced into the analysis and consideration is given to the treatment of discrete and continuous design parameters. Improved designs are identified using both methods and verified with further CFD analyses, and the benefits of each method are discussed
Quantitative information flow under generic leakage functions and adaptive adversaries
We put forward a model of action-based randomization mechanisms to analyse
quantitative information flow (QIF) under generic leakage functions, and under
possibly adaptive adversaries. This model subsumes many of the QIF models
proposed so far. Our main contributions include the following: (1) we identify
mild general conditions on the leakage function under which it is possible to
derive general and significant results on adaptive QIF; (2) we contrast the
efficiency of adaptive and non-adaptive strategies, showing that the latter are
as efficient as the former in terms of length up to an expansion factor bounded
by the number of available actions; (3) we show that the maximum information
leakage over strategies, given a finite time horizon, can be expressed in terms
of a Bellman equation. This can be used to compute an optimal finite strategy
recursively, by resorting to standard methods like backward induction.Comment: Revised and extended version of conference paper with the same title
appeared in Proc. of FORTE 2014, LNC
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