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About 10,000 papers for “Adaptive algorithm”

Fast adaptive ESPRIT algorithm

Roland Badeau, Gaël Richard, Bertrand David · HAL (Le Centre pour la Communication Scientifique Directe) · 2005

International audience

Cited by 0Open access

Finger motion adaptive algorithm

Ulusoy Melda · Figshare · 2015

Raw dataset obtained from experiments with sighted people using the finger motion adaptive algorithm. Videos captured during mechanical experiments and Braille eperiments. Texts T1 and T2 are used in the experiments performed with blind subjects.

Cited by 0Open access

Adaptive brain extraction algorithm.

Steffen Stoewer (335833), Jozien Goense (335834), Georgios A. Keliris (335835) et al. · Figshare · 2015

<p>Demonstration of the effects of the algorithm implemented in fMRI Sandbox on an example slice (#10) of the mean image of a sample dataset M1: a) before applying any mask or filter; b) after applying a filter to suppress matter outside the brain; c) after brain extraction, now containing intensity changes in the brain due to the prior filtering; d) after a mask with the extraction data derived from c) has been applied to the original dataset.</p>

Cited by 0Open access

An adaptive DNN Partition Algorithm Greedy Algorithm (ADPA)

ZhangLei · 2021

To solve the problem of DNN running too slowly on mobile terminals. The traditional approach is to use the edge server to cooperate with deep reasoning. However, this approach cannot be adapted to the partition according to the characteristics of each layer of the network and DNN, and can not achieve satisfactory reasoning acceleration effect. In this paper, an adaptive DNN partition algorithm is proposed to reduce the running time of DNN by considering the characteristics of each DNN layer and adaptively selecting the computing devices that should be divided and deployed in each layer. Experimental results show that the algorithm can effectively reduce the running time of DNN in different network environments and meet the delay requirements.

Cited by 0

Adaptive Multi-Population Optimization Algorithm

Zhixi Li · Code Ocean · 2021

This is the source code (in Python) of an useful metaheuristic optimization algorithm called the Adaptive Multi-Population Optimization (AMPO) for continuous optimization problems.

Cited by 0Open access

An Adaptive Ant Clustering Algorithm

Xu Xiao · 2006

Enlightened by the behaviors of gregarious ant colonies, an artificial ant movement (AM) model and an adaptive ant clustering (AAC) algorithm for this model are presented. In the algorithm, each ant is treated as an agent to represent a data object. In the AM model, each ant has two states: sleeping state and active state. In the algorithm AAC, the ant’s state is controlled by both a function of the ant’s fitness to the environment it locates and a probability function for the ants becoming active. By moving dynamically, the ants form different subgroups adaptively, and consequently the whole ant group dynamically self-organizes into distinctive and independent subgroups within which highly similar ants are closely connected. The result of data objects clustering is therefore achieved. This paper also present a method to adaptively update the parameters and the ants’ local movement strategies which greatly improve the speed and the quality of clustering. Experimental results show that the AAC algorithm on the AM model is much superior to other ant clustering methods such as BM and LF in terms of computational cost, speed and quality. It is adaptive, robust and efficient, and achieves high autonomy, simplicity and efficiency. It is suitable for solving high dimensional and complicated clustering problems.

Cited by 0

Adaptive harmony PSO search algorithm

Zou De-xuan · 2010

For the purpose of avoiding the disadvantage of improved harmony search (IHS) algorithm,an adaptive harmony search-particle swarm optimization (AHSPSO) algorithm is presented. In the harmony memory of AHSPSO,every variable is updated by using the PSO algorithm,then adjusted by adaptive parameters PAR and bw. So this algorithm can improve the search efficiency of multidimension problems. Finally,the proposed AHSPSO algorithm is tested and contrasted with IHS,PSO,and SA. The test results show the favorable abilities of accuracy and escaping local minimums.

Cited by 5

Adaptive Block Coordinate DIRECT algorithm

Tao, Qinghua, Huang, Xiaolin, Wang, Shuning et al. · arXiv (Cornell University) · 2016

DIviding RECTangles (DIRECT) is an efficient and popular method in dealing with bound constrained optimization problems. However, DIRECT suffers from dimension curse, since its computational complexity soars when dimension increases. Besides, DIRECT also converges slowly when the objective function is flat. In this paper, we propose a coordinate DIRECT algorithm, which coincides with the spirits of other coordinate update algorithms. We transform the original problem into a series of sub-problems, where only one or several coordinates are selected to optimize and the rest keeps fixed. For each sub-problem, coordinately dividing the feasible domain enjoys low computational burden. Besides, we develop adaptive schemes to keep the efficiency and flexibility to tackle different functions. Specifically, we use block coordinate update, of which the size could be adaptively selected, and we also employ sequential quadratic programming (SQP) to conduct the local search to efficiently accelerate the convergence even when the objective function is flat. With these techniques, the proposed algorithm achieves promising performance on both efficiency and accuracy in numerical experiments.

Cited by 0Open access

NeatSort - A practical adaptive algorithm

Marcello La Rocca, Domenico Cantone · arXiv (Cornell University) · 2014

We present a new adaptive sorting algorithm which is optimal for most disorder metrics and, more important, has a simple and quick implementation. On input $X$, our algorithm has a theoretical $Ω(|X|)$ lower bound and a $\mathcal{O}(|X|\log|X|)$ upper bound, exhibiting amazing adaptive properties which makes it run closer to its lower bound as disorder (computed on different metrics) diminishes. From a practical point of view, \textit{NeatSort} has proven itself competitive with (and often better than) \textit{qsort} and any \textit{Random Quicksort} implementation, even on random arrays.

Cited by 1Open access

Adaptive playfair cipher Crypto algorithm

Salam Abdulkhaleq Noaman · Journal of Al-Qadisiyah for Computer Science and Mathematics · 2017

A well known cryptographic techniques is Playfair Cryptography, it is considered one of the classical method. After the invention of different techniques, it is easy to break Playfair. This paper proposed some way for removal of the traditional Playfair drawbacks. The Adaptive playfair algorithm proposed in this paper, add more security and complexity to the classical playfair algorithm. In addition to the use of two keys in form of matrices to encrypt the message, the proposed method works depending on using the odd even positions for the every pairs of the letters. The odd pairs encrypt through the first matrix key and the even pairs encrypt by using the second matrix, then applying XOR function with the third key to the result. The resulting cipher text will be in binary form, the plain text obtained by run proposed step backwards.

Cited by 3Open access

Adaptive H-REMD simulation algorithm.

Danial Pourjafar-Dehkordi (7165523), Martin Zacharias (9025) · Figshare · 2021

<p>(<b>a)</b> The python library and the Amber MD package are initialized using a batch file that contains the input parameters. There is no biasing force inserted to the system in the first interval. At the end of each interval the trajectories are passed to the library and CVs are calculated. Based on their values in the previous runs the biasing force is updated. The H-REMD simulation then continues with the updated biasing potential introduced in the replicas. The cycle goes on until the simulation is stopped. (<b>b</b>) Illustration of the biasing potential acting on an exemplary CV vs. time. The value of CV is coloured based on the simulation interval. At the beginning the biasing along the CV is localized to the vicinity of the initially sampled regime eventually with sampling of new regions the biasing potential extends to other regions along the CV. The overall bias potential (solid black line) is the sum of three Gaussians (dashed grey lines). Note, that in the reference replica (replica 1) there is no biasing and the biasing increases incrementally with increasing replica number.</p>

Cited by 0Open access

The EELRU adaptive replacement algorithm �

Yannis Smaragdakis A, Scott Kaplan B, Paul Wilson C · 2002

The wide performance gap between processors and disks ensures that effective page replacement remains an important consideration in modern systems. This paper presents early eviction LRU (EELRU), an adaptive replacement algorithm. EELRU uses aggregate recency information to recognize the reference behavior of a workload and to adjust its speed of adaptation. An on-line cost/benefit analysis guides replacement decisions. This analysis is based on the LRU stack model (LRUSM) of program behavior. Essentially, EELRU is an on-line approximation of an optimal algorithm for the LRUSM. We prove that EELRU offers strong theoretical guarantees of performance relative to the LRU replacement algorithm. EELRU can never be more than a factor of 3 worse than LRU, while in a common best case it can be better than LRU by a large factor (proportional to the number of pages in memory). The goal of EELRU is to provide a simple replacement algorithm that adapts to reference patterns at all scales. Thus, EELRU should perform well for a wider range of programs and memory sizes than other algorithms. Practical experiments validate this claim. For a large number of programs and wide ranges of memory sizes, we show that EELRU outperforms LRU, typically reducing misses by 10–30%, and occasionally by much more—sometimes by a factor of 2–10. It rarely performs worse than LRU, and then only by a small amount.

Cited by 0