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Статті в журналах з теми "Pure adaptive search"
Baritompa, W. P., Zhang Baoping, R. H. Mladineo, G. R. Wood, and Z. B. Zabinsky. "Towards Pure Adaptive Search." Journal of Global Optimization 7, no. 1 (July 1995): 93–110. http://dx.doi.org/10.1007/bf01100207.
Повний текст джерелаZabinsky, Zelda B., and Robert L. Smith. "Pure adaptive search in global optimization." Mathematical Programming 53, no. 1-3 (January 1992): 323–38. http://dx.doi.org/10.1007/bf01585710.
Повний текст джерелаPatel, Nitin R., Robert L. Smith, and Zelda B. Zabinsky. "Pure adaptive search in monte carlo optimization." Mathematical Programming 43, no. 1-3 (January 1989): 317–28. http://dx.doi.org/10.1007/bf01582296.
Повний текст джерелаZabinsky, Z. B., G. R. Wood, M. A. Steel, and W. P. Baritompa. "Pure adaptive search for finite global optimization." Mathematical Programming 69, no. 1-3 (July 1995): 443–48. http://dx.doi.org/10.1007/bf01585570.
Повний текст джерелаBulger, D., W. P. Baritompa, and G. R. Wood. "Implementing Pure Adaptive Search with Grover's Quantum Algorithm." Journal of Optimization Theory and Applications 116, no. 3 (March 2003): 517–29. http://dx.doi.org/10.1023/a:1023061218864.
Повний текст джерелаSimić, Dragan, Vasa Svirčević, Vladimir Ilin, Svetislav D. Simić, and Svetlana Simić. "Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management." Cybernetics and Systems 50, no. 1 (January 2, 2019): 58–77. http://dx.doi.org/10.1080/01969722.2018.1558014.
Повний текст джерелаSALAHI, M. "A SELF-REGULAR NEWTON BASED ALGORITHM FOR LINEAR OPTIMIZATION." ANZIAM Journal 51, no. 2 (October 2009): 286–301. http://dx.doi.org/10.1017/s1446181109000340.
Повний текст джерелаDinh, Bach, Thang Nguyen, Nguyen Quynh, and Le Dai. "A Novel Method for Economic Dispatch of Combined Heat and Power Generation." Energies 11, no. 11 (November 10, 2018): 3113. http://dx.doi.org/10.3390/en11113113.
Повний текст джерелаChen, Fu Xing, and Xu Sheng Xie. "Application on Query of Distributed Database Based on Improved Genetic Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4617–21. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4617.
Повний текст джерелаAngora, G., M. Brescia, S. Cavuoti, M. Paolillo, G. Longo, M. Cantiello, M. Capaccioli, et al. "Astroinformatics-based search for globular clusters in the Fornax Deep Survey." Monthly Notices of the Royal Astronomical Society 490, no. 3 (October 7, 2019): 4080–106. http://dx.doi.org/10.1093/mnras/stz2801.
Повний текст джерелаДисертації з теми "Pure adaptive search"
(13992058), David W. Bulger. "Stochastic global optimisation algorithms." Thesis, 1996. https://figshare.com/articles/thesis/Stochastic_global_optimisation_algorithms/21377646.
Повний текст джерелаThis thesis addresses aspects of stochastic algorithms for the solution of global optimisation problems. The bulk of the research investigates algorithm models of the adaptive search variety. Performances of stochastic and deterministic algorithms are also compared.
Chapter 2 defines pure adaptive search, the prototypical improving region search scheme from the literature. Analyses from the literature of the search duration of pure adaptive search in two specialised situations are recounted and interpreted. In each case pure adaptive search is shown to require an expected search time which varies only linearly with the dimension of the feasible region.
In Chapter 3 a generalisation of pure adaptive search is introduced under the name of hesitant adaptive search. This original algorithm retains the sample point generation mechanism of pure adaptive search, but allows for hesitation, in which an algorithm iteration passes without an improving sample being located. In this way hesitant adaptive search is intended to model more closely practically implementable algorithms. The analysis of the convergence of hesitant adaptive search is more thorough than the analyses already appearing in the literature, as it describes how hesitant adaptive search behaves when given more general objective functions than in previous studies. By specialising to the case of pure adaptive search we obtain a unification of the results appearing in those papers.
Chapter 4 is the most applied of the chapters in this thesis. Here hesitant adaptive search is specialised to describe the convergence behaviour of localisation search schemes. A localisation search scheme produces a bracket of the current improving region at each iteration. The results of Chapter 3 are applied to find necessary and sufficient conditions on the 'tightness' of the brackets to ensure that the dependence of the expected search duration on the dimension of the feasible region is linear, quadratic, cubic, and so forth.
Chapter 5 describes another original generalisation of pure adaptive search, known as fenestral adaptive search. This algorithm generates sample points from a region determined not merely by the previous sample, but by the previous w samples, where w is some prespecified positive integer. The expected search duration of fenestral adaptive search is greater than that of pure adaptive search, but still varies only linearly with the dimension of the feasible region. The sequence of objective function values obtained constitutes an interesting stochastic process, and Chapter 5 is devoted to understanding this process.
Chapter 6 presents a theoretical comparison of the search durations of deterministic and stochastic global optimisation algorithms, together with some discussion of the implications. It is shown that to any stochastic algorithm, there corresponds a deterministic algorithm which requires no more iterations on average, but we discuss why stochastic algorithms may still be more efficient than their deterministic counterparts in practice.
Частини книг з теми "Pure adaptive search"
Zabinsky, Zelda B. "Pure Random Search and Pure Adaptive Search." In Nonconvex Optimization and Its Applications, 25–54. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9182-9_2.
Повний текст джерелаZabinsky, Z. B., and B. P. Kristinsdottir. "Complexity Analysis Integrating Pure Adaptive Search (PAS) and Pure Random Search (PRS)." In Nonconvex Optimization and Its Applications, 171–81. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2600-8_11.
Повний текст джерелаТези доповідей конференцій з теми "Pure adaptive search"
Schmeiser, Bruce W., and Jin Wang. "On the performance of pure adaptive search." In the 27th conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/224401.224634.
Повний текст джерелаLin, T. W., Jianyi Lu, Jian Lin, and Don A. Gregory. "Adaptive learning of binary patterns by using correlation processes." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.tud4.
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