Relevant bibliographies by topics / Adaptive algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Adaptive algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Adaptive algorithm"

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Litvinchuk, Yuliia. "Self-adaptive CMA-ES Algorithm." Mathematical and computer modelling. Series: Physical and mathematical sciences 24 (December 5, 2023): 81–90. http://dx.doi.org/10.32626/2308-5878.2023-24.81-90.

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This article will consider one of the self-adaptive algorithms for selecting parameters of complex systems, examples of which are neural networks. Self-adaptive algorithms are algorithms that change their behavior at runtime based on available information and predetermined reward mechanisms. These algorithms are widely used in various fields, including machine learning, optimization, and data compression. The self-adaptiveness of the algorithm in this case will be based on the selection of the number of peaks in the mixture of distributions in the extended CMA-ES algorithm under the condition of a normal base distribution. The work presents an improved self-adaptive CMA-ES algorithm, with an emphasis on the parameter that selects the number of pixels in a mixture of normal distributions. The algorithm takes into account the methods of setting this optimal value, which is used when choosing cluster numbers in the CURE, BIRCH, etc. clustering algorithms. It is obvious that the given justification of this approach can be extended to mixtures with a different base distribution, each of which is characterized by a skin number of peaks in the mixture distribution. This implies self-adaptability and applicability of the algorithm to a wider range of scenarios involving different distribution characteristics. There is no doubt that the proposed sado-adaptive parameter setting algorithm, based on the CMA-ES algorithm, can be extended to other genetic and evolutionary algorithms that include the selection of additional chromosomes (individuals) during the transition between iteration epochs of the algorithm. Another feature of the proposed algorithm is the use of theoretical foundations of cluster analysis to estimate the number of peaks in the distribution of chromosomes. This approach is widely used in the latest self-adaptive algorithms for determining the initial parameters (hyperparameters) of complex systems
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Zhang, Zhaoxia. "Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm." International Journal of Intelligent Information Technologies 20, no. 1 (May 17, 2024): 1–19. http://dx.doi.org/10.4018/ijiit.344024.

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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 590–97. http://dx.doi.org/10.1609/aaai.v34i01.5398.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization." Journal of Artificial Intelligence Research 74 (May 26, 2022): 303–51. http://dx.doi.org/10.1613/jair.1.12997.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1 − 1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1−1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1 − 1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular diffusion model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
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O'Malley, Lawrence V. "Adaptive clustering algorithm." IBM Journal of Research and Development 29, no. 1 (January 1985): 68–72. http://dx.doi.org/10.1147/rd.291.0068.

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Kusuma, Purba Daru, and Meta Kallista. "Adaptive Cone Algorithm." International Journal on Advanced Science, Engineering and Information Technology 13, no. 5 (October 31, 2023): 1605. http://dx.doi.org/10.18517/ijaseit.13.5.18284.

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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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Sarma, Priyanka, and Atowar Ul Islam. "Implementation of Hybrid Adaptive Learning Algorithm for Task Offloading." Indian Journal Of Science And Technology 17, no. 38 (October 8, 2024): 3929–36. http://dx.doi.org/10.17485/ijst/v17i38.2280.

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Objectives: Offloading tasks in edge computing (EC) plays an important role in optimizing resource utilization and enhancing the system performance. This paper studies various AI-based computation offloading (CO) strategies and proposes a hybrid Adaptive Learning Algorithm, that reduces the latency significantly compared to traditional RL-based on-policy and off-policy algorithms namely Q-Learning (QL) and State Action Reward State Action (SARSA) for CO in an EC environment. The paper evaluates and compares the efficiency of the proposed algorithms in optimizing dynamic offloading decisions, focusing on factors such as latency. Methods: The research is inspired by existing literature that has discovered the various applications of QL and SARSA in CO and mobile EC. This paper inspects how these algorithms perform in terms of reducing overall latency and proposes an adaptive hybrid algorithm. Novelty: Although other AI-based techniques exist in earlier research on CO in EC, the primary novelty proposed is the addition of a hybrid adaptive RL algorithm. Factors impacting the task offloading decision are crucial in the proposed algorithm. Moreover, the decision is not based only on static policies; the current state is the driving factor for decision-making. Findings: The exploration and exploitation handshake is very promising in reducing latency, as seen in the simulation results. The proposed algorithm outperforms Q-Learning and SARSA in terms of latency. Keywords: Computation offloading, Mobile Edge Computing, Reinforcement learning, Q­learning, SARSA
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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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Xi, Zichen. "Analysis of Adaptive Equalization Algorithms." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 295–305. http://dx.doi.org/10.54097/hset.v70i.12477.

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Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed visually. Finally, a table is listed to compare the three commonly used algorithms. From the aspects of practicability and application environment, it deeply analyzes the limitations of traditional adaptive equalization algorithms such as LMS and CMA in the current era, and demonstrates the superior performance of neural networks. On this basis, this paper emphasizes the powerful learning ability of neural networks and the opportunities for future research, which will lay the foundation for the development of next-generation communication networks.
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Dissertations / Theses on the topic "Adaptive algorithm"

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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 <i>proof of a set expression</i> 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 <i>E</i> are, we define a measurement for determining how difficult it is for <i>E</i> 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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Law, Nga Lam. "Parameter-free adaptive genetic algorithm /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202007%20LAW.

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au, Daniel Schubert@csiro, and Daniel Schubert. "A Multivariate Adaptive Trimmed Likelihood Algorithm." Murdoch University, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20061019.132720.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is also shown to behave independently of the type of outlier, for example, whether applied to a data set with a solitary observation located in some extreme region or to a data set composed of clusters of outlying data, this algorithm performs with a high probability of success.
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Schubert, Daniel Dice. "A multivariate adaptive trimmed likelihood algorithm /." Access via Murdoch University Digital Theses Project, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20061019.132720.

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Vogt, Paul E. "An adaptive multi-scene correlation algorithm." Master's thesis, University of Central Florida, 1988. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/76421.

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University of Central Florida College of Engineering Thesis<br>Digital scene matching algorithms have been used in both military and commercial image processing systems for years. The trend toward using multiple sensors in military imaging systems has generated anew interest in real time techniques to accomplish sensor fusion tasks such as field of view alignment. This thesis analyzes methods presently in use and intorduces a novel algorithm that improves scene correlation performance. The focus of the new technique is in the segmentation area, where significant features are extracted from background and clutter. These performance improvements are espeically helpful when the scene contains excessive noise and or lacks detail, a trouble spot for standard correlation systems. The restrictions imposed on the system design include implementations possible for real time porcessing and a minimum of hardware and power consimption. Simulations of the algorithms programmed for an image processing board by an IBM personal computer are discussed.<br>M.S.<br>Masters<br>Engineering<br>Engineering<br>79 p.
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Turner, Steven Primitivo. "Adaptive out of step relay algorithm." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-01242009-063244/.

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Schubert, Daniel. "A multivariate adaptive trimmed likelihood algorithm." Thesis, Schubert, Daniel (2005) A multivariate adaptive trimmed likelihood algorithm. PhD thesis, Murdoch University, 2005. https://researchrepository.murdoch.edu.au/id/eprint/295/.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is also shown to behave independently of the type of outlier, for example, whether applied to a data set with a solitary observation located in some extreme region or to a data set composed of clusters of outlying data, this algorithm performs with a high probability of success.
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Schubert, Daniel. "A multivariate adaptive trimmed likelihood algorithm." Schubert, Daniel (2005) A multivariate adaptive trimmed likelihood algorithm. PhD thesis, Murdoch University, 2005. http://researchrepository.murdoch.edu.au/295/.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is also shown to behave independently of the type of outlier, for example, whether applied to a data set with a solitary observation located in some extreme region or to a data set composed of clusters of outlying data, this algorithm performs with a high probability of success.
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Doss, Christopher. "Algorithm Partitioning and Scheduling for Adaptive Computers." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010619-175307.

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<p>Adaptive, or reconfigurable, computing has emerged as a viable computing alternative for computationally intense applications. (We use the terms adaptive and reconfigurable interchangeably). Here, an adaptive computer is a computing system that contains a general purpose processor attached to a programmable logic device such as a field programmable gate array (FPGA). These computing systems combine the flexibility of general purpose processors with the speed of application specific processors. The computer system designer can cater the hardware to a specific application by modifying the configuration of the FPGAs. The designer can reconfigure the FPGAs at some future time for other applications since the FPGAs do not have a fixed structure.Several reconfigurable computers have been implemented to demonstrate the viability of reconfigurable processors.Applications mapped to these processors include pattern recognition in high-energy physics, statistical physics and genetic optimization algorithms. In many cases, the reconfigurable computing implementation provided thehighest performance, in terms of execution speed, published (at the respective time).To achieve such performance, the application must effectively utilize the available resources. This presents a challenge for software designers, who are generally used to mapping applications onto fixed computing systems.Generally, the designers examine the available hardware resources and modify their application accordingly. With reconfigurable computers, the available resources can be generated when needed. While it may seem thatthis flexibility would ease the mapping process, it actually introduces new problems, such as what components should be allocated, and how many of each component should be used to generate the best performance. With conventionalhardware components, these questions were not an issue.In addition, software engineers are generally not adept at hardware design.In this dissertation, we present a design methodology for systematically implementing computationally intense applications on reconfigurable computing systems. This methodology is based on concepts from compiler theory to ease automation.In addition to the design methodology, we present, a toolthat implements a significant portion of the design methodology. RAS can be considered as a module generation tool for assisting the design process. Given a flow graph representing a loop nest, RAS allocates a set of resources, and schedules the nodes of the graph to the resources. RAS also generates an estimate of the amount of time it would take if the design implemented according to the schedule.This dissertation also presents results of designs produced by RAS. Multiple tests were performed using three computationally intense algorithms. RAS mapped the algorithms to five configurations representingdifferent sets of resource constraints. Two of the configurations were based on actual systems used in the research development, while the remainingthree were hypothetical systems based on other components available in the market. Experimental results from RASindicate that a significant amount of speedup is attainable using the allocated resources with the given schedule.<P>
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Pervaiz, Mehtab M. "Spatio-temporal adaptive algorithm for reacting flows." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/34994.

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Books on the topic "Adaptive algorithm"

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R, Hanebutte Ulf, and Langley Research Center, eds. A parallel adaptive mesh refinement algorithm. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Birtles, B. The BRESTART self adaptive optimum start algorithm. Watford: Building Research Establishment, 1985.

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O'Toole, Gregory J. An investigation of the UTIAS adaptive washout algorithm. Ottawa: National Library of Canada, 1995.

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G, Hills Richard, and United States. National Aeronautics and Space Administration., eds. An adaptive grid algorithm for one-dimensional nonlinear equations. [Washington, DC: National Aeronautics and Space Administration, 1990.

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Shapiro, Richard A., ed. Adaptive Finite Element Solution Algorithm for the Euler Equations. Wiesbaden: Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-322-87879-3.

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Ashby, Steven F. ChebyCode, a FORTRAN implementation of Manteuffel's adaptive Chebyshev algorithm. Urbana, IL (1304 W. Springfield Ave., Urbana 61801-2987): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1985.

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Shapiro, Richard A. Adaptive finite element solution algorithm for the Euler equations. Braunschweig: Vieweg, 1991.

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Zaharia, Ramona. Adaptive compression algorithm for full colour three dimensional integral images. Leicester: De Montfort University, 2001.

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United States. National Aeronautics and Space Administration., ed. The minimal time detection algorithm. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Rogers, David. G/SPINES: A hybird of friedman's multivariate adaptive regression splines (MARS) algorithm with Holland's genetic algorithm. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1991.

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Book chapters on the topic "Adaptive algorithm"

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Tan, Ying. "Adaptive Fireworks Algorithm." In Fireworks Algorithm, 119–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46353-6_8.

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Williams, Ross N. "The DHPC Algorithm." In Adaptive Data Compression, 107–24. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_2.

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Williams, Ross N. "A Multimodal Algorithm." In Adaptive Data Compression, 245–81. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_5.

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Williams, Ross N. "An Experimental Adaptive Algorithm." In Adaptive Data Compression, 145–244. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_4.

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Diniz, Paulo Sergio Ramirez. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering, 71–131. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering, 61–102. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_3.

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Diniz, Paulo S. R. "Quantization Effects in the LMS Algorithm." In Adaptive Filtering, 591–603. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_15.

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Diniz, Paulo S. R. "Quantization Effects in the RLS Algorithm." In Adaptive Filtering, 605–21. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_16.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering, 79–135. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering, 1–54. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_3.

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Conference papers on the topic "Adaptive algorithm"

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Trabia, Mohamed B., and Xiao Bin Lu. "A Fuzzy Adaptive Simplex Search Optimization Algorithm." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8586.

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Abstract Optimization algorithms usually use fixed parameters that are empirically chosen to reach the minimum for various objective functions. This paper shows how to incorporate fuzzy logic in optimization algorithms to make the search adaptive to various objective functions. This idea is applied to produce a new algorithm for minimization of a function of n variables using an adaptive form of the simplex method. The search starts by generating a simplex with n+1 vertices. The algorithm replaces the point with the highest function value by a new point. This process comprises reflecting the point with the highest function value in addition to expanding or contracting the simplex using fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. The efficiency of the algorithm is studied using a set of standard minimization test problems. This algorithm generally results in a faster convergence toward the minimum. The algorithm is also applied successfully to two engineering design problems.
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Kanev, F. Yu, V. P. Lukin, and L. N. Lavrinova. "Dynamic Adaptive Mirror in the Algorithm of Phase Conjugation." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.tua52.

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Considering numerically the problem of adaptive compensation of intensive laser beam atmospheric aberrations we demonstrated the influence of transient processes that occur in adaptive systems on the effectiveness and rate of control.
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Feng, Xu, and Wenjian Yu. "A Fast Adaptive Randomized PCA Algorithm." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/411.

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It is desirable to adaptively determine the number of dimensions (rank) for PCA according to a given tolerance of low-rank approximation error. In this work, we aim to develop a fast algorithm solving this adaptive PCA problem. We propose to replace the QR factorization in randQB_EI algorithm with matrix multiplication and inversion of small matrices, and propose a new error indicator to incrementally evaluate approximation error in Frobenius norm. Combining the shifted power iteration technique for better accuracy, we finally build up an algorithm named farPCA. Experimental results show that farPCA is much faster than the baseline methods (randQB_EI, randUBV and svds) in practical setting of multi-thread computing, while producing nearly optimal results of adpative PCA.
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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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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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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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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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Tran, Tan D., Canh V. Pham, Dung T. K. Ha, and Phuong N. H. Pham. "Improved Parallel Algorithm for Non-Monotone Submodular Maximization under Knapsack Constraint." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/217.

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This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm improves the best approximation factor of the existing parallel one from 8 to 7 with O(log n) adaptive complexity. The key idea of our approach is to create an alternate threshold algorithmic framework. This new strategy alternately constructs two disjoint candidate solutions within a constant number of sequence rounds. Then, the algorithm boosts solution quality without sacrificing the adaptive complexity. Extensive experimental studies on three applications, Revenue Maximization, Image Summarization, and Maximum Weighted Cut, show that our algorithm not only significantly increases solution quality but also requires comparative adaptivity to state-of-the-art algorithms.
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Gyorodi, C., R. Gyorodi, M. Pater, O. Boc, and Z. David. "Adaptive AFOPT algorithm." In Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05). IEEE, 2005. http://dx.doi.org/10.1109/synasc.2005.17.

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Li, Junzhi, Shaoqiu Zheng, and Ying Tan. "Adaptive Fireworks Algorithm." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900418.

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Reports on the topic "Adaptive algorithm"

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Allen, Donald S., Yang-Woo Kim, and Meenakshi Pasupathy. Forecasting with an Adaptive Control Algorithm. Federal Reserve Bank of St. Louis, 1996. http://dx.doi.org/10.20955/wp.1996.009.

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Saltzman, J. S., D. L. Brown, K. D. Brislawn, G. S. Chesshire, D. J. Quinlan, and M. Berger. Adaptive mesh refinement algorithm development and dissemination. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/515637.

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Scannapieco, Anthony J. An Adaptive Mesh Algorithm: Mesh Structure and Generation. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1291247.

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Scannapieco, Anthony J. An Adaptive Mesh Algorithm: Mapping the Mesh Variables. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1304744.

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Chang, Hyeong S., Michael C. Fu, and Steven I. Marcus. An Adaptive Sampling Algorithm for Solving Markov Decision Processes. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada438505.

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Torrieri, Don. The Anticipative Maximum Adaptive-Array Algorithm for Frequency-Hopping Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada448152.

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Havens, Tim C., Dominic K. Ho, Justin Farrell, James M. Keller, Mihail Popescu, Tuan T. Ton, and David C. Wong. Locally Adaptive Detection Algorithm for Forward-Looking Ground-Penetrating Radar. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada545174.

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Hatke, Gary F., and Stuart C. Schwartz. A Robust Adaptive Array Structure Using the Soft Constrained LMS algorithm. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada203959.

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Kim, Dai H. A Highly Functional Decision Paradigm Based on Nonlinear Adaptive Genetic Algorithm. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada281457.

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Luk, Franklin T., and Sanzheng Qiao. Analysis of a Linearly Constrained Least Squares Algorithm for Adaptive Beamforming. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255017.

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