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Journal articles on the topic 'Random search'

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Author: Grafiati
Published: 4 June 2021
Last updated: 29 May 2022

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1

Vose, Michael D. "Random heuristic search." Theoretical Computer Science 229, no. 1-2 (November 1999): 103–42. http://dx.doi.org/10.1016/s0304-3975(99)00120-6.

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2

Devroye, Luc, James King, and Colin McDiarmid. "Random Hyperplane Search Trees." SIAM Journal on Computing 38, no. 6 (January 2009): 2411–25. http://dx.doi.org/10.1137/060678609.

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3

Devroye, Luc, and Ralph Neininger. "Random suffix search trees." Random Structures and Algorithms 23, no. 4 (November 11, 2003): 357–96. http://dx.doi.org/10.1002/rsa.10103.

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4

Sandev, Trifce, Alexander Iomin, and Ljupco Kocarev. "Random search on comb." Journal of Physics A: Mathematical and Theoretical 52, no. 46 (October 23, 2019): 465001. http://dx.doi.org/10.1088/1751-8121/ab4a2c.

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5

Appel, M. J., R. LaBarre, and D. Radulovic. "On Accelerated Random Search." SIAM Journal on Optimization 14, no. 3 (January 2004): 708–31. http://dx.doi.org/10.1137/s105262340240063x.

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6

Vincent, Patrick, and Izhak Rubin. "Cooperative search versus random search using UAV swarms." IFAC Proceedings Volumes 37, no. 8 (July 2004): 944–49. http://dx.doi.org/10.1016/s1474-6670(17)32102-x.

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7

Abakuks, A. "THEORY OF GLOBAL RANDOM SEARCH." Bulletin of the London Mathematical Society 24, no. 4 (July 1992): 413–14. http://dx.doi.org/10.1112/blms/24.4.413.

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8

Friedrich, Benjamin M. "Search along persistent random walks." Physical Biology 5, no. 2 (June 24, 2008): 026007. http://dx.doi.org/10.1088/1478-3975/5/2/026007.

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9

MacGregor, James, and Eric Lee. "Menu search: random or systematic?" International Journal of Man-Machine Studies 26, no. 5 (May 1987): 627–31. http://dx.doi.org/10.1016/s0020-7373(87)80075-5.

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10

Ali, M. M., and C. Storey. "Modified controlled random search algorithms." International Journal of Computer Mathematics 53, no. 3-4 (January 1994): 229–35. http://dx.doi.org/10.1080/00207169408804329.

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11

Sebő, András. "On two random search problems." Journal of Statistical Planning and Inference 11, no. 1 (January 1985): 23–31. http://dx.doi.org/10.1016/0378-3758(85)90022-9.

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12

Devroye, Luc, and Ralph Neininger. "Distances and Finger Search in Random Binary Search Trees." SIAM Journal on Computing 33, no. 3 (January 2004): 647–58. http://dx.doi.org/10.1137/s0097539703424521.

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13

Chan, Alan H. S., and Alan J. Courtney. "Revising and Validating the Random Search Model for Competitive Search." Perceptual and Motor Skills 87, no. 1 (August 1998): 251–60. http://dx.doi.org/10.2466/pms.1998.87.1.251.

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A random search model was fit to a total of 2592 visual search times on a single-target detection task. By using a competing homogeneous background and uniform stimulus material, specifying viewing distance, controlling the presentation of search task material, and eliminating some options for extreme search strategies, very high correlation coefficients were found when a random search model was fit to both the individual data and to pooled data. A response time parameter was incorporated into the traditional random-search model and very good predictions of search performance were obtained.
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14

Zhang, Yu-Chao, Wan-Su Bao, Xiang Wang, and Xiang-Qun Fu. "Optimized quantum random-walk search algorithm for multi-solution search." Chinese Physics B 24, no. 11 (November 2015): 110309. http://dx.doi.org/10.1088/1674-1056/24/11/110309.

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15

Li, Jun. "A random dynamic search algorithm research." Journal of Computational Methods in Sciences and Engineering 19, no. 3 (July 17, 2019): 659–72. http://dx.doi.org/10.3233/jcm-193522.

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16

Wang, Minghui, and Heping Zhang. "Search for the smallest random forest." Statistics and Its Interface 2, no. 3 (2009): 381–88. http://dx.doi.org/10.4310/sii.2009.v2.n3.a11.

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17

Florea, Adrian-Catalin, and Razvan Andonie. "Weighted Random Search for Hyperparameter Optimization." International Journal of Computers Communications & Control 14, no. 2 (April 14, 2019): 154–69. http://dx.doi.org/10.15837/ijccc.2019.2.3514.

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We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem dened on a discrete domain.
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18

Zhaoqing, Jia, You Jinyuan, Rao Ruonan, and Li Minglu. "Random walk search in unstructured P2P." Journal of Systems Engineering and Electronics 17, no. 3 (September 2006): 648–53. http://dx.doi.org/10.1016/s1004-4132(06)60111-4.

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19

Vose, Michael D. "Logarithmic Convergence of Random Heuristic Search." Evolutionary Computation 4, no. 4 (December 1996): 395–404. http://dx.doi.org/10.1162/evco.1996.4.4.395.

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This paper speaks to the inherent emergent behavior of genetic search. For completeness and generality, a class of stochastic search algorithms, random heuristic search, is reviewed. A general convergence theorem for this class is then proved. Since the simple genetic algorithm (GA) is an instance of random heuristic search, a corollary is a result concerning GAs and time to convergence.
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20

Combettes, P. L., and H. J. Trussell. "Set theoretic estimation by random search." IEEE Transactions on Signal Processing 39, no. 7 (July 1991): 1669–71. http://dx.doi.org/10.1109/78.134403.

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21

Flajolet, Philippe, Xavier Gourdon, and Conrado Mart�nez. "Patterns in random binary search trees." Random Structures and Algorithms 11, no. 3 (October 1997): 223–44. http://dx.doi.org/10.1002/(sici)1098-2418(199710)11:3<223::aid-rsa2>3.0.co;2-2.

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22

Li, Chunshien, Roland Priemer, and Kuo-Hsiang Cheng. "Optimization by random search with jumps." International Journal for Numerical Methods in Engineering 60, no. 7 (June 8, 2004): 1301–15. http://dx.doi.org/10.1002/nme.1014.

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23

Kahn, Lawrence M., and Stuart A. Low. "Systematic and Random Search A Synthesis." Journal of Human Resources 23, no. 1 (1988): 1. http://dx.doi.org/10.2307/145841.

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24

Teamah, Abd El Moneim AnwarMohamed, Hamdy Mohamed Abou Gabal, and Mohamed Abd Allah El Hadidy. "Random search in a bounded area." International Journal of Mathematics in Operational Research 10, no. 2 (2017): 137. http://dx.doi.org/10.1504/ijmor.2017.081921.

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25

El Hadidy, Mohamed Abd Allah, Abd El Moneim AnwarMohamed Teamah, and Hamdy Mohamed Abou Gabal. "Random search in a bounded area." International Journal of Mathematics in Operational Research 10, no. 2 (2017): 137. http://dx.doi.org/10.1504/ijmor.2017.10002018.

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26

Abdelrahman, Omer H., and Erol Gelenbe. "Search in non-homogenous random environments?" ACM SIGMETRICS Performance Evaluation Review 39, no. 3 (December 21, 2011): 37–39. http://dx.doi.org/10.1145/2160803.2160853.

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27

Arcaute, Esteban, Ning Chen, Ravi Kumar, David Liben-Nowell, Mohammad Mahdian, Hamid Nazerzadeh, and Ying Xu. "Deterministic Decentralized Search in Random Graphs." Internet Mathematics 5, no. 1-2 (January 2008): 141–54. http://dx.doi.org/10.1080/15427951.2008.10129298.

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28

Aguech, Rafik, Nabil Lasmar, and Hosam Mahmoud. "Distances in random digital search trees." Acta Informatica 43, no. 4 (September 22, 2006): 243–64. http://dx.doi.org/10.1007/s00236-006-0019-7.

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29

Price, C. J., M. Reale, and B. L. Robertson. "One side cut accelerated random search." Optimization Letters 8, no. 3 (March 17, 2013): 1137–48. http://dx.doi.org/10.1007/s11590-013-0631-8.

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30

Charilogis, Vasileios, Ioannis Tsoulos, Alexandros Tzallas, and Nikolaos Anastasopoulos. "An Improved Controlled Random Search Method." Symmetry 13, no. 11 (October 20, 2021): 1981. http://dx.doi.org/10.3390/sym13111981.

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A modified version of a common global optimization method named controlled random search is presented here. This method is designed to estimate the global minimum of multidimensional symmetric and asymmetric functional problems. The new method modifies the original algorithm by incorporating a new sampling method, a new termination rule and the periodical application of a local search optimization algorithm to the points sampled. The new version is compared against the original using some benchmark functions from the relevant literature.
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31

Zhu, Chenbo, Jie Xu, Chun-Hung Chen, Loo Hay Lee, and Jian-Qiang Hu. "Balancing Search and Estimation in Random Search Based Stochastic Simulation Optimization." IEEE Transactions on Automatic Control 61, no. 11 (November 2016): 3593–98. http://dx.doi.org/10.1109/tac.2016.2522094.

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32

Chan, Alan H. S., and C. Y. Chan. "Validating the random search model for a double-target search task." Theoretical Issues in Ergonomics Science 1, no. 2 (January 2000): 157–67. http://dx.doi.org/10.1080/14639220050171315.

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33

Judson, R. S., M. E. Colvin, J. C. Meza, A. Huffer, and D. Gutierrez. "Do intelligent configuration search techniques outperform random search for large molecules?" International Journal of Quantum Chemistry 44, no. 2 (September 5, 1992): 277–90. http://dx.doi.org/10.1002/qua.560440214.

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34

Hadidy, Mohamed Abd Allah El, and Mohamed Abd El Hady Kassem. "On minimum expected search time for a multiplicative random search problem." International Journal of Operational Research 29, no. 2 (2017): 219. http://dx.doi.org/10.1504/ijor.2017.083957.

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35

El Hadidy, Mohamed Abd Allah, and Mohamed Abd El Hady Kassem. "On minimum expected search time for a multiplicative random search problem." International Journal of Operational Research 29, no. 2 (2017): 219. http://dx.doi.org/10.1504/ijor.2017.10004614.

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36

Dor, Avner, and Eitan Greenshtein. "An Almost-Greedy Search on Random Binary Vectors and Random Graphs." Journal of Algorithms 40, no. 1 (July 2001): 102–33. http://dx.doi.org/10.1006/jagm.2000.1140.

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37

Hu, Jinglu, Kotaro Hirasawa, and Junichi Murata. "RasID - Random Search for Neural Network Training." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 4 (August 20, 1998): 134–41. http://dx.doi.org/10.20965/jaciii.1998.p0134.

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This paper presents a novel random search, RasID, for neural network training, that introduces a sophisticated probability density function (PDF) in a random search for generating search vectors. The PDF provides two parameters to control local search ranges and directions efficiently. This realizes an intensified search where it is easy to find good solutions locally or a diversified search to escape local minima based on success-failure of past searches. Local gradients, if available, and trend information on the criterion function surface are used to improve search performance. The proposed scheme is applied to layered neural network training and is benchmarked against deterministic and nondeterministic methods.
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38

Zhou, Ruilin. "Currency Exchange in a Random Search Model." Review of Economic Studies 64, no. 2 (April 1997): 289. http://dx.doi.org/10.2307/2971713.

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39

Serrano, Will. "Smart Internet Search with Random Neural Networks." European Review 25, no. 2 (February 6, 2017): 260–72. http://dx.doi.org/10.1017/s1062798716000594.

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Web services that are free of charge to users typically offer access to online information based on some form of economic interest of the web service itself. Advertisers who put the information on the web will make a payment to the search services based on the clicks that their advertisements receive. Thus, end users cannot know that the results they obtain from web search engines are exhaustive, or that they actually respond to their needs. To fill the gap between user needs and the information presented to them on the web, Intelligent Search Assistants have been proposed to act at the interface between users and search engines to present data to users in a manner that reflects their actual needs or their observed or stated preferences. This paper presents an Intelligent Internet Search Assistant based on the Random Neural Network that tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a ‘relevance metric’ to compare the performance of our Intelligent Internet Search Assistant against a few search engines, showing that it provides better performance.
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40

Getty, Thomas, and H. R. Pulliam. "Random Prey Detection with Pause-Travel Search." American Naturalist 138, no. 6 (December 1991): 1459–77. http://dx.doi.org/10.1086/285296.

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41

Aldous, David. "Book Review: Evolution of random search trees." Bulletin of the American Mathematical Society 27, no. 2 (October 1, 1992): 313–16. http://dx.doi.org/10.1090/s0273-0979-1992-00314-2.

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42

Pillans, John. "Improved Analog Filter Design by Random Search." IEEE Transactions on Circuits and Systems I: Regular Papers 66, no. 6 (June 2019): 2350–60. http://dx.doi.org/10.1109/tcsi.2019.2893616.

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43

O’Keeffe, Kevin, Paolo Santi, Brandon Wang, and Carlo Ratti. "Urban sensing as a random search process." Physica A: Statistical Mechanics and its Applications 562 (January 2021): 125307. http://dx.doi.org/10.1016/j.physa.2020.125307.

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44

Pasanen, Tomi A. "Random binary search tree with equal elements." Theoretical Computer Science 411, no. 43 (October 2010): 3867–72. http://dx.doi.org/10.1016/j.tcs.2010.06.023.

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45

Sakiyama, Tomoko, and Yukio-Pegio Gunji. "Optimal random search using limited spatial memory." Royal Society Open Science 5, no. 3 (March 2018): 171057. http://dx.doi.org/10.1098/rsos.171057.

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Lévy walks are known to be efficient movements because Lévy walkers search wide areas while restricting returns to previously visited sites. A self-avoiding walk (SAW) is a series of moves on a lattice that visit the same place only once. As such, SAWs can also be effective search algorithms. However, it is not realistic that foragers memorize many visited positions for a long time. In this work, we investigated whether foragers performed optimal searches when having limited memory. The agent in our model followed SAWs to some extent by memorizing and avoiding visited places. However, the agent lost its memory after a while. In that situation, the agent changed its reactions to visited patches by considering global trail patterns based on local memorized information. As a result, we succeeded in making the agent occasionally produce ballistic walks related to power-law tailed movements across some ranges.
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46

Yang Li. "Gesture Search: Random Access to Smartphone Content." IEEE Pervasive Computing 11, no. 1 (2012): 10–13. http://dx.doi.org/10.1109/mprv.2012.9.

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47

Bo Tang, Z. "Adaptive partitioned random search to global optimization." IEEE Transactions on Automatic Control 39, no. 11 (1994): 2235–44. http://dx.doi.org/10.1109/9.333768.

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48

Andradóttir, Sigrún, and Andrei A. Prudius. "Adaptive random search for continuous simulation optimization." Naval Research Logistics (NRL) 57, no. 6 (July 23, 2010): 583–604. http://dx.doi.org/10.1002/nav.20422.

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49

Imoto, Taiji. "Effective protein folding in simple random search." Biopolymers 58, no. 1 (2000): 46–49. http://dx.doi.org/10.1002/1097-0282(200101)58:1<46::aid-bip50>3.0.co;2-q.

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50

Luus, Rein, and Paul Brenek. "Incorporation of gradient into random search optimization." Chemical Engineering & Technology - CET 12, no. 1 (1989): 309–18. http://dx.doi.org/10.1002/ceat.270120143.

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