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Relevant bibliographies by topics / Adaptive algorithms

Academic literature on the topic 'Adaptive algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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Journal articles on the topic "Adaptive algorithms"

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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.

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Carstensen, Carsten, and Rob Stevenson. "Adaptive Algorithms." Oberwolfach Reports 13, no. 3 (2016): 2513–70. http://dx.doi.org/10.4171/owr/2016/44.

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Bhardwaj, Sumit, DR Vinod Shokeen, and Arun kumar. "A Review on Adaptive Equalizer Algorithms." International Journal of Scientific Research 3, no. 7 (June 1, 2012): 187–89. http://dx.doi.org/10.15373/22778179/july2014/59.

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Hu, Yingkang, Kirill A. Kopotun., and Xiang Ming Yu. "Modified Adaptive Algorithms." SIAM Journal on Numerical Analysis 38, no. 3 (January 2000): 1013–33. http://dx.doi.org/10.1137/s0036142999353569.

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Krommer, Arnold R., and Christoph W. Ueberhuber. "Architecture adaptive algorithms." Parallel Computing 19, no. 4 (April 1993): 409–35. http://dx.doi.org/10.1016/0167-8191(93)90055-p.

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Guan, Sihai, Qing Cheng, Yong Zhao, and Bharat Biswal. "Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function." PLOS ONE 16, no. 10 (October 11, 2021): e0258155. http://dx.doi.org/10.1371/journal.pone.0258155.

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Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.

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Huy, Nguyen Quang, Ong Yew Soon, Lim Meng Hiot, and Natalio Krasnogor. "Adaptive Cellular Memetic Algorithms." Evolutionary Computation 17, no. 2 (June 2009): 231–56. http://dx.doi.org/10.1162/evco.2009.17.2.231.

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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

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LAWLOR, DAVID, YANG WANG, and ANDREW CHRISTLIEB. "ADAPTIVE SUB-LINEAR TIME FOURIER ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 01 (January 2013): 1350003. http://dx.doi.org/10.1142/s1793536913500039.

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We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k ≪ N significant Fourier coefficients from a signal of bandwidth N. Previous deterministic algorithms exhibit quadratic runtime scaling, while our algorithm scales linearly with k in the average case. Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function. This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies. We show that empirically our algorithm is orders of magnitude faster than competing algorithms.

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Uddin, Zahoor, Ayaz Ahmad, Muhammad Iqbal, and Zeeshan Kaleem. "Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems." Mobile Information Systems 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7038531.

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Independent component analysis (ICA) is a technique of blind source separation (BSS) used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA) algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA) algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM) and binary phase shift keying (BPSK) signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR) and input data block lengths.

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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, Jie Liu, Xiaowei Guo, Chunye Gong, Bo Yang, and Xukai Fan. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

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Dissertations / Theses on the topic "Adaptive algorithms"

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Abu-Bakar, Nordin. "Adaptive genetic algorithms." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343268.

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Mirzazadeh, Mehdi. "Adaptive Comparison-Based Algorithms for Evaluating Set Queries." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1147.

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In this thesis we study a problem that arises in answering boolean queries submitted to a search engine. Usually a search engine stores the set of IDs of documents containing each word in a pre-computed sorted order and to evaluate a query like "computer AND science" the search engine has to evaluate the union of the sets of documents containing the words "computer" and "science". More complex queries will result in more complex set expressions. In this thesis we consider the problem of evaluation of a set expression with union and intersection as operators and ordered sets as operands. We explore properties of comparison-based algorithms for the problem. A proof of a set expression is the set of comparisons that a comparison-based algorithm performs before it can determine the result of the expression. We discuss the properties of the proofs of set expressions and based on how complex the smallest proofs of a set expression E are, we define a measurement for determining how difficult it is for E to be computed. Then, we design an algorithm that is adaptive to the difficulty of the input expression and we show that the running time of the algorithm is roughly proportional to difficulty of the input expression, where the factor is roughly logarithmic in the number of the operands of the input expression.

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Vlajic, Natalija J. "Adaptive algorithms for hypertext clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ32276.pdf.

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Sequeira, Armando M. P. de Jesus. "Adaptive two dimensional RLS algorithms." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25653.

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Shah, Ijteba-ul-Hasnain. "Constrained adaptive natural gradient algorithms for adaptive array processing." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15646.

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Stone, Joseph Carlyle. "Adaptive discrete-ordinates algorithms and strategies." Texas A&M University, 2007. http://hdl.handle.net/1969.1/85857.

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The approaches for discretizing the direction variable in particle transport calculations are the discrete-ordinates method and function-expansion methods. Both approaches are limited if the transport solution is not smooth. Angular discretization errors in the discrete-ordinates method arise from the inability of a given quadrature set to accurately perform the needed integrals over the direction ("angular") domain. We propose that an adaptive discrete-ordinate algorithm will be useful in many problems of practical interest. We start with a "base quadrature set" and add quadrature points as needed in order to resolve the angular flux function. We compare an interpolated angular-flux value against a calculated value. If the values are within a user specified tolerance, the point is not added; otherwise it is. Upon the addition of a point we must recalculate weights. Our interpolatory functions map angular-flux values at the quadrature directions to a continuous function that can be evaluated at any direction. We force our quadrature weights to be consistent with these functions in the sense that the quadrature integral of the angular flux is the exact integral of the interpolatory function (a finite-element methodology that determines coefficients by collocation instead of the usual weightedresidual procedure). We demonstrate our approach in two-dimensional Cartesian geometry, focusing on the azimuthal direction The interpolative methods we test are simple linear, linear in sine and cosine, an Abu-Shumays â baseâ quadrature with a simple linear adaptive and an Abu-Shumays â baseâ quadrature with a linear in sine and cosine adaptive. In the latter two methods the local refinement does not reduce the ability of the base set to integrate high-order spherical harmonics (important in problems with highly anisotropic scattering). We utilize a variety of one-group test problems to demonstrate that in all cases, angular discretization errors (including "ray effects") can be eliminated to whatever tolerance the user requests. We further demonstrate through detailed quantitative analysis that local refinement does indeed produce a more efficient placement of unknowns. We conclude that this work introduces a very promising approach to a long-standing problem in deterministic transport, and we believe it will lead to fruitful avenues of further investigation.

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Bate, Stephen Donald. "Adaptive coding algorithms for data transmission." Thesis, Coventry University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303388.

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Korejo, Imtiaz Ali. "Adaptive mutation operators for evolutionary algorithms." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10315.

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Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.

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Wilstrup, Steven L. "Adaptive algorithms for two dimensional filtering." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22855.

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Nambiar, Raghu. "Learning algorithms for adaptive digital filtering." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/5544/.

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In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme.

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Books on the topic "Adaptive algorithms"

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Kaufman, Howard, Itzhak Barkana, and Kenneth Sobel. Direct Adaptive Control Algorithms. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0657-6.

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Benveniste, Albert. Adaptive algorithms and stochastic approximations. Berlin: Springer-Verlag, 1990.

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Benveniste, Albert, Michel Métivier, and Pierre Priouret. Adaptive Algorithms and Stochastic Approximations. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75894-2.

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Beliczynski, Bartlomiej, Andrzej Dzielinski, Marcin Iwanowski, and Bernardete Ribeiro, eds. Adaptive and Natural Computing Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71618-1.

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Beliczynski, Bartlomiej, Andrzej Dzielinski, Marcin Iwanowski, and Bernardete Ribeiro, eds. Adaptive and Natural Computing Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71629-7.

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Dawid, Herbert. Adaptive Learning by Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-00211-7.

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Bern, Marshall W., Joseph E. Flaherty, and Mitchell Luskin, eds. Grid Generation and Adaptive Algorithms. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1556-1.

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Dobnikar, Andrej, Uroš Lotrič, and Branko Šter, eds. Adaptive and Natural Computing Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20267-4.

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Dobnikar, Andrej, Uroš Lotrič, and Branko Šter, eds. Adaptive and Natural Computing Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20282-7.

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Dawid, Herbert. Adaptive Learning by Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-18142-9.

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Book chapters on the topic "Adaptive algorithms"

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Dhoked, Sahil, Wojciech Golab, and Neeraj Mittal. "Adaptive Algorithms." In Recoverable Mutual Exclusion, 63–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20002-1_7.

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Diniz, Paulo Sergio Ramirez. "LMS-Based Algorithms." In Adaptive Filtering, 133–81. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_4.

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Diniz, Paulo S. R. "LMS-Based Algorithms." In Adaptive Filtering, 103–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_4.

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Diniz, Paulo S. R. "LMS-Based Algorithms." In Adaptive Filtering, 137–207. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_4.

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Diniz, Paulo S. R. "Lms-Based Algorithms." In Adaptive Filtering, 1–63. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_4.

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Deng, Ping, Weili Wu, Eugene Shragowitz, and Ding-Zhu Du. "Adaptive Partitions." In Encyclopedia of Algorithms, 18–22. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_2.

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Deng, Ping, Weili Wu, Eugene Shragowitz, and Ding-Zhu Du. "Adaptive Partitions." In Encyclopedia of Algorithms, 1–6. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_2-2.

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Deng, Ping, Weili Wu, and Eugene Shragowitz. "Adaptive Partitions." In Encyclopedia of Algorithms, 4–7. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_2.

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Diniz, Paulo Sergio Ramirez. "Fast Transversal RLS Algorithms." In Adaptive Filtering, 289–309. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_7.

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Diniz, Paulo S. R. "Fast Transversal RLS Algorithms." In Adaptive Filtering, 263–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_8.

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Conference papers on the topic "Adaptive algorithms"

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Rhoadarmer, Troy A., and Brent L. Ellerbroek. "Optimization of Closed-Loop Adaptive Optics Wavefront Reconstruction Algorithms Using Experimentally Measured Performance Data: Experimental Results." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.fa7.

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Experiments have shown the reward adaptive optics provides in improving the resolution of ground-based astronomical telescopes.1-2 A critical contributor to adaptive optics system performance is the reconstruction algorithm that converts wavefront sensor (WFS) measurements into the deformable mirror (DM) actuator commands.3-4 Minimum variance reconstruction algorithms have been developed extensively to optimize the performance of adaptive optics systems given specific atmospheric conditions.5-7 These algorithms depend on atmospheric parameters which are seldom known exactly and are constantly fluctuating. This is especially true for systems incorporating multiple WFS beacons that require knowledge of the wind speed profile, the vertical distribution of atmospheric turbulence, and the intensity of the wavefront sensing beacons to calculate the optimal reconstructor.7 This uncertainty and continual changing of atmospheric conditions implies that an optimal degree of turbulence compensation cannot be achieved or maintained over long time intervals with static reconstructor coefficients. A need exists for a method of updating these coefficients in real time based on actual closed-loop performance.

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Ellerbroek, Brent L., and Troy A. Rhoadarmer. "Optimization of Closed-Loop Adaptive-Optics Control Algorithms Using Measured Performance Data: Experimental Results." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athb.2.

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Experiments have shown the reward adaptive-optics provides in improving the resolution of ground-based astronomical telescopes [1,2,3]. A critical contributor to adaptive-optics system performance is the control algorithm that converts wavefront sensor (WFS) measurements into the deformable mirror (DM) actuator commands. For the adaptive-optics systems in use today this control algorithm consists of a wavefront reconstruction step to estimate the instantaneous phase distortion to be compensated [4], followed by a servo control law to temporally filter this instantaneous estimate before it is applied to the deformable mirror [5]. So-called modal adaptive-optics systems can apply different temporal filters to separate spatial components, or modes, of the overall phase distortion [6]. Extensive analysis has been performed to evaluate and optimize the performance of these adaptive-optics control systems [7,8,9,10,11], but the results obtained depend on atmospheric parameters which are seldom known exactly and are constantly fluctuating. The uncertainty and variability of atmospheric conditions implies that an optimal degree of turbulence compensation cannot be achieved or maintained for long time intervals with a fixed control algorithm. A need exists for methods to update adaptive-optics control algorithms based upon actual system performance. Encouraging results have already been obtained demonstrating the value of emperically optimizing the control bandwidths for a modal adaptive-optics system [12]. In comparison, the subject of real-time adjustments to reconstruction matrices on the basis of measured system performance has received little attention.

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Vick, Andrew J. A. "Fast Centroiding Algorithms for CCD based Shack-Hartmann Sensors." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.pd6.

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Bender, Michael A., Roozbeh Ebrahimi, Jeremy T. Fineman, Golnaz Ghasemiesfeh, Rob Johnson, and Samuel McCauley. "Cache-Adaptive Algorithms." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.71.

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Butenko, Lev N., Alexander G. Seregin, and Peter A. Fridman. "The algorithms for measurement of displacement of white-light fringes." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.tua18.

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A white-light source is used so that good interference occurs only when the two paths are equal, that is, when both primary mirror’s segments are the same distance away from the interferometer, e.g. with a double-image prism in the adaptive telescope. When observing a small step between surfaces with comparable reference dimensions, interference fringes are usually formed in white light that are perpendicular to the boundary line between the surfaces. An interferometer with white-light source is based on a prism design that includes a rectangular prism with a beamspliter hypotenuse face to which an additional prism is cemented to form a double-image compound pentaprism. It makes possible to monitor the step between the elements of a compound primary mirror more simply than in an interferometer with Kosters prism. This is because of the almost complete compensation of the optical path length in a system with counterpropagation of the interfering beams. Preliminary phasing of primary mirror segments during assembling process may be accomplished with help of a such ambiguity sensor. The experimental investigations with visual control showed a possibility to recognize displacement of 1/20 wavelength. This investigation was fulfilled at the special test bench with high resolution laser interferometer. The interferometer resolution is 1 nm.

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Pruidze, D. V., J. C. Ricklin, D. G. Voelz, and M. A. Vorontsov. "Adaptive Correction of Phase-Distorted Extended Source Images: Experimental Results." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athb.3.

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We demonstrate a new type of adaptive imaging system capable of improving the quality of phase-distorted images of extended objects. The operational algorithm was based on optimization of the spectral image quality criteria suggested in [1]. For adaptive control of the nine-electrode semi-passive bimorph mirror we used gradient optimization algorithms. To introduce slowly varying large scale phase distortions into the imaging system a second deformable mirror with computer control was used. Small scale phase distortions were created using the nonlinear optics technique described in [2]. Image quality criteria were measured using the optical image quality analyzer described in [1].

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Yang, Peng, Peilin Zhao, and Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.

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Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full information label is provided for each node, which is violated in many practical applications where labeling is expensive and the oracle may only tell whether the prediction is correct or not. To address this issue, we propose a bandit online algorithm on graphs. It derives per-instance confidence region of the prediction, from which the model can be learned adaptively to minimize the online regret. Experiments on benchmark graph datasets show that the proposed bandit algorithm outperforms state-of-the-art competitors, even sometimes beats the algorithms using full information label feedback.

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Szydlowski, Wieslaw M., and Carl A. Nelson. "Synthesis of Adaptive Pliers Mechanism Using Genetic Algorithms." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35514.

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A Genetic Algorithms (GA) approach to dimensional synthesis of an adaptive pliers mechanism is presented. Satisfaction functions are implemented in a custom C++ algorithm to enforce ergonomic and force-transmission goals. Using various values of selection rate and crossover rate, along with a “diversity monitor,” many solutions are found which are functionally superior to the original mechanism.

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Ghosh, Aritra, and Andrew Lan. "BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/332.

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Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.

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Gao, X., S. Ovaska, and X. Wang. "Genetic Algorithms-based Detector Generation in Negative Selection Algorithm." In 2006 IEEE Mountain Workshop on Adaptive and Learning Systems. IEEE, 2006. http://dx.doi.org/10.1109/smcals.2006.250704.

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Reports on the topic "Adaptive algorithms"

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James, Conrad D. Hardware Acceleration of Adaptive Neural Algorithms. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1410778.

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Jones, M. T., and P. E. Plassmann. Parallel algorithms for adaptive mesh refinement. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/515612.

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Varshney, Pramod K., Chilukuri K. Mohan, and Krishan G. Mehrotra. Adaptive Models and Fusion Algorithms for Information Exploitation. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada516533.

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Bodson, Marc. Adaptive Algorithms for Active Noise and Vibration Control. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390623.

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Brown, D. L., D. J. Quinlan, G. S. Chesshire, W. D. Henshaw, and M. Berndt. Adaptive composite overlapping mesh algorithms on message passing architectures. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/518933.

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Polak, Elijah, and Michael Wetter. Generalized pattern search algorithms with adaptive precision function evaluations. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/813385.

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Smith, Sharon L., and Robert B. Schnabel. Centralized and Distributed Dynamic Scheduling for Adaptive, Parallel Algorithms. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada233557.

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Parsons, I., and J. Solberg. Adaptive Mesh Refinement Algorithms for Parallel Unstructured Finite Element Codes. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/899100.

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Xiu, Dongbin. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1258292.

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Moore, Frank, Pat Marshall, and Eric Balster. Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada427113.

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