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Journal articles on the topic 'Search problems'

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Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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1

Ryan, J. D., Rudolf Ahlswede, and Ingold Wegener. "Search Problems." Journal of the Operational Research Society 39, no. 10 (October 1988): 977. http://dx.doi.org/10.2307/2583050.

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2

Dykstra, Richard, Rudolph Ahlswede, Ingo Wegener, and Jean E. Wotschke. "Search Problems." Journal of the American Statistical Association 84, no. 406 (June 1989): 627. http://dx.doi.org/10.2307/2289975.

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3

Ryan, J. D. "Search Problems." Journal of the Operational Research Society 39, no. 10 (October 1988): 977–78. http://dx.doi.org/10.1057/jors.1988.165.

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4

Cannings, C., R. Ahlswede, and I. Wegener. "Search Problems." Statistician 37, no. 4/5 (1988): 483. http://dx.doi.org/10.2307/2348782.

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5

Kummer, Martin, and Frank Stephan. "Effective Search Problems." Mathematical Logic Quarterly 40, no. 2 (1994): 224–36. http://dx.doi.org/10.1002/malq.19940400209.

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6

Lunegov, S. V., and N. N. Petrov. "Search Problems on Graphs." IFAC Proceedings Volumes 34, no. 6 (July 2001): 969–71. http://dx.doi.org/10.1016/s1474-6670(17)35307-7.

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7

Hinderer, Karl, and Michael Stieglitz. "On polychotomous search problems." European Journal of Operational Research 73, no. 2 (March 1994): 279–94. http://dx.doi.org/10.1016/0377-2217(94)90265-8.

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8

Aigner, M. "Search problems on graphs." Discrete Applied Mathematics 14, no. 3 (July 1986): 215–30. http://dx.doi.org/10.1016/0166-218x(86)90026-0.

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9

Neledva, N., and S. Linenko. "GENERAL PROBLEMS OF SUSPECT SEARCH." “International Humanitarian University Herald. Jurisprudence”, no. 50 (2021): 100–103. http://dx.doi.org/10.32841/2307-1745.2021.50.21.

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10

Prough, Donald S., and Mark H. Zornow. "Solutions in search of problems." Critical Care Medicine 24, no. 7 (July 1996): 1104–5. http://dx.doi.org/10.1097/00003246-199607000-00006.

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11

Wah, Guo-jie Li, and Chee Fen Yu. "Multiprocessing of Combinatorial Search Problems." Computer 18, no. 6 (June 1985): 93–108. http://dx.doi.org/10.1109/mc.1985.1662926.

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12

Grover, Lov K. "Quantum Search on Structured Problems." Chaos, Solitons & Fractals 10, no. 10 (September 1999): 1695–705. http://dx.doi.org/10.1016/s0960-0779(98)00217-3.

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13

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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14

Héger, Tamás, Balázs Patkós, and Marcella Takáts. "Search problems in vector spaces." Designs, Codes and Cryptography 76, no. 2 (March 5, 2014): 207–16. http://dx.doi.org/10.1007/s10623-014-9941-9.

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15

Yang, Boting, and Yi Cao. "Monotonicity in digraph search problems." Theoretical Computer Science 407, no. 1-3 (November 2008): 532–44. http://dx.doi.org/10.1016/j.tcs.2008.08.025.

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16

Beimel, Amos, Paz Carmi, Kobbi Nissim, and Enav Weinreb. "Private Approximation of Search Problems." SIAM Journal on Computing 38, no. 5 (January 2009): 1728–60. http://dx.doi.org/10.1137/060671899.

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17

Blass, Andreas, and Yuri Gurevich. "Randomizing Reductions of Search Problems." SIAM Journal on Computing 22, no. 5 (October 1993): 949–75. http://dx.doi.org/10.1137/0222059.

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18

Norouzi, Yaghoub, and Hoda Homavandi. "Image search and retrieval problems in web search engines." Online Information Review 42, no. 6 (October 8, 2018): 752–67. http://dx.doi.org/10.1108/oir-01-2017-0007.

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PurposeThe purpose of this paper is to investigate image search and retrieval problems in selected search engines in relation to Persian writing style challenges.Design/methodology/approachThis study is an applied one, and to answer the questions the authors used an evaluative research method. The aim of the research is to explore the morphological and semantic problems of Persian language in connection with image search and retrieval among the three major and widespread search engines: Google, Yahoo and Bing. In order to collect the data, a checklist designed by the researcher was used and then the data were analyzed by descriptive and inferential statistics.FindingsThe results indicate that Google, Yahoo and Bing search engines do not pay enough attention to morphological and semantic features of Persian language in image search and retrieval. This research reveals that six groups of Persian language features include derived words, derived/compound words, Persian and Arabic Plural words, use of dotted T and the use of spoken language and polysemy, which are the major problems in this area. In addition, the results suggest that Google is the best search engine of all in terms of compatibility with Persian language features.Originality/valueThis study investigated some new aspects of the above-mentioned subject through combining morphological and semantic aspects of Persian language with image search and retrieval. Therefore, this study is an interdisciplinary research, the results of which would help both to offer some solutions and to carry out similar research on this subject area. This study will also fill a gap in research studies conducted so far in this area in Farsi language, especially in image search and retrieval. Moreover, findings of this study can help to bridge the gap between the user’s questions and search engines (systems) retrievals. In addition, the methodology of this paper provides a framework for further research on image search and retrieval in databases and search engines.
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19

Agnetis, Alessandro, Ben Hermans, Roel Leus, and Salim Rostami. "Time-critical testing and search problems." European Journal of Operational Research 296, no. 2 (January 2022): 440–52. http://dx.doi.org/10.1016/j.ejor.2021.03.038.

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20

McDonnell, Judith, Frank R. Scarpitti, and F. Kurt Cylke. "Social Problems: The Search for Solutions." Teaching Sociology 25, no. 1 (January 1997): 89. http://dx.doi.org/10.2307/1319122.

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21

Alm, Jeremy F., and David A. Andrews. "Doceamus: Solutions in Search of Problems." Notices of the American Mathematical Society 59, no. 07 (August 1, 2012): 1. http://dx.doi.org/10.1090/noti870.

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22

Van Hentenryck, Pascal. "Constraint programming for combinatorial search problems." ACM Computing Surveys 28, no. 4es (December 1996): 76. http://dx.doi.org/10.1145/242224.242322.

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23

Preston, Mark D., Jonathan W. Pitchford, and A. Jamie Wood. "Evolutionary optimality in stochastic search problems." Journal of The Royal Society Interface 7, no. 50 (March 24, 2010): 1301–10. http://dx.doi.org/10.1098/rsif.2010.0090.

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‘Optimal’ behaviour in a biological system is not simply that which maximizes a mean, or temporally and spatially averaged, fitness function. Rather, population dynamics and demographic and environmental stochasticity are fundamental evolutionary ingredients. Here, we revisit the problem of optimal foraging, where some recent studies claim that organisms should forage according to Lévy walks. We show that, in an ecological scenario dominated by uncertainty and high mortality, Lévy walks can indeed be evolutionarily favourable. However, this conclusion is dependent on the definition of efficiency and the details of the simulations. We analyse measures of efficiency that incorporate population-level characteristics, such as variance, superdiffusivity and heavy tails, and compare the results with those generated by simple maximizing of the average encounter rate. These results have implications on stochastic search problems in general, and also on computational models of evolutionary optima.
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24

Kerschke, P., H. Wang, M. Preuss, C. Grimme, A. H. Deutz, H. Trautmann, and M. T. M. Emmerich. "Search Dynamics on Multimodal Multiobjective Problems." Evolutionary Computation 27, no. 4 (December 2019): 577–609. http://dx.doi.org/10.1162/evco_a_00234.

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We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.
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25

Al-Betar, Mohammed Azmi, Ahamad Tajudin Khader, Mohammed A. Awadallah, Mahmmoud Hafsaldin Alawan, and Belal Zaqaibeh. "Cellular Harmony Search for Optimization Problems." Journal of Applied Mathematics 2013 (2013): 1–20. http://dx.doi.org/10.1155/2013/139464.

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Structured population in evolutionary algorithms (EAs) is an important research track where an individual only interacts with its neighboring individuals in the breeding step. The main rationale behind this is to provide a high level of diversity to overcome the genetic drift. Cellular automata concepts have been embedded to the process of EA in order to provide a decentralized method in order to preserve the population structure. Harmony search (HS) is a recent EA that considers the whole individuals in the breeding step. In this paper, the cellular automata concepts are embedded into the HS algorithm to come up with a new version called cellular harmony search (cHS). In cHS, the population is arranged as a two-dimensional toroidal grid, where each individual in the grid is a cell and only interacts with its neighbors. The memory consideration and population update are modified according to cellular EA theory. The experimental results using benchmark functions show that embedding the cellular automata concepts with HS processes directly affects the performance. Finally, a parameter sensitivity analysis of the cHS variation is analyzed and a comparative evaluation shows the success of cHS.
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26

Maddox, John. "Abstract models in search of problems." Nature 365, no. 6446 (October 1993): 485. http://dx.doi.org/10.1038/365485a0.

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27

Gasarch, William I., and Katia S. Guimarães. "Binary search and recursive graph problems." Theoretical Computer Science 181, no. 1 (July 1997): 119–39. http://dx.doi.org/10.1016/s0304-3975(96)00266-6.

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28

Fomin, F. V. "Helicopter search problems, bandwidth and pathwidth." Discrete Applied Mathematics 85, no. 1 (June 1998): 59–70. http://dx.doi.org/10.1016/s0166-218x(97)00131-5.

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29

Kumar, Vijay, Jitender Kumar Chhabra, and Dinesh Kumar. "Differential Search Algorithm for Multiobjective Problems." Procedia Computer Science 48 (2015): 22–28. http://dx.doi.org/10.1016/j.procs.2015.04.105.

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30

RODOŠEK, R. "A Search Control for Scheduling Problems." Automatica 34, no. 6 (June 1998): 741–50. http://dx.doi.org/10.1016/s0005-1098(98)80003-4.

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31

Rodošek, R. "A Search Control for Scheduling Problems." IFAC Proceedings Volumes 29, no. 7 (November 1996): 159–65. http://dx.doi.org/10.1016/s1474-6670(17)43715-3.

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32

Reinefeld, Alexander. "Parallel search in discrete optimization problems." Simulation Practice and Theory 4, no. 2-3 (May 1996): 169–88. http://dx.doi.org/10.1016/0928-4869(95)00036-4.

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33

Lokutsievskii, L. V. "Accumulation of switchings in search problems." Journal of Mathematical Sciences 151, no. 6 (June 2008): 3497–505. http://dx.doi.org/10.1007/s10958-008-9044-0.

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34

Salomon, Marc, Roelof Kuik, and Luk N. Van Wassenhove. "Statistical search methods for lotsizing problems." Annals of Operations Research 41, no. 4 (December 1993): 453–68. http://dx.doi.org/10.1007/bf02023005.

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35

Zhou, J. L., and A. L. Tits. "Nonmonotone line search for minimax problems." Journal of Optimization Theory and Applications 76, no. 3 (March 1993): 455–76. http://dx.doi.org/10.1007/bf00939377.

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36

Abril, Montserrat, Miguel A. Salido, and Federico Barber. "Distributed search in railway scheduling problems." Engineering Applications of Artificial Intelligence 21, no. 5 (August 2008): 744–55. http://dx.doi.org/10.1016/j.engappai.2008.03.008.

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37

PAREJATOBES, P., D. PELTA, A. SANCHOROYO, and J. VERDEGAY. "Search spaces representation in optimization problems." Expert Systems with Applications 34, no. 4 (May 2008): 2891–95. http://dx.doi.org/10.1016/j.eswa.2007.05.046.

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38

Hazon, Noam, Yonatan Aumann, Sarit Kraus, and David Sarne. "Physical search problems with probabilistic knowledge." Artificial Intelligence 196 (March 2013): 26–52. http://dx.doi.org/10.1016/j.artint.2012.12.003.

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39

Hanika, Jiří. "Herbrandizing search problems in Bounded Arithmetic." MLQ 50, no. 6 (October 2004): 577–86. http://dx.doi.org/10.1002/malq.200410005.

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40

GOMES, CARLA, and BART SELMAN. "HYBRID SEARCH STRATEGIES FOR HETEROGENEOUS SEARCH SPACES." International Journal on Artificial Intelligence Tools 09, no. 01 (March 2000): 45–57. http://dx.doi.org/10.1142/s0218213000000057.

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Recently, there has been much interest in enhancing purely combinatorial formalisms with numerical information. For example, planning formalisms can be enriched by taking resource constraints and probabilistic information into account. The Mixed Integer Programming (MIP) paradigm from operations research provides a natural tool for solving optimization problems that combine such numeric and non-numeric information. The MIP approach relies heavily on linear program relaxations and branch-and-bound search. This is in contrast with depth-first or iterative deepening strategies generally used in artificial intelligence. We provide a detailed characterization of the structure of the underlying search spaces as explored by these search strategies. Our analysis shows that much can be gained by combining different search strategies for solving hard MIP problems, thereby leveraging each strategy's strength in terms of the combinatorial and numeric information.
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41

Hassanien, Aboul Ella, Ahmad Taher Azar, and Mohammed A. El Shorbagy. "Circular Local Search for Unconstrained Optimization Problems." International Journal of Advanced Intelligence Paradigms 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijaip.2018.10038186.

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42

Bhattacharjee, Kuntal. "Economic Dispatch Problems Using Backtracking Search Optimization." International Journal of Energy Optimization and Engineering 7, no. 2 (April 2018): 39–60. http://dx.doi.org/10.4018/ijeoe.2018040102.

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The purpose of this article is to present a backtracking search optimization technique (BSA) to determine the feasible optimum solution of the economic load dispatch (ELD) problems involving different realistic equality and inequality constraints, such as power balance, ramp rate limits, and prohibited operating zone constraints. Effects of valve-point loading, multi-fuel option of large-scale thermal plants, system transmission loss are also taken into consideration for more realistic application. Two effective operations, mutation and crossover, help BSA algorithms to find the global solution for different optimization problems. BSA has the capability to deal with multimodal problems due to its powerful exploration and exploitation capability. BSA is free from sensitive parameter control operations. Simulation results set up the proposed approach in a better stage compared to several other existing optimization techniques in terms quality of solution and computational efficiency. Results also reveal the robustness of the proposed methodology.
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43

Hollister, L. "Problems in the Search for Cognition Enhancers." Pharmacopsychiatry 23, S 2 (February 1990): 33–36. http://dx.doi.org/10.1055/s-2007-1014529.

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44

Ohlemuller, M. "Tabu Search for Large Location-Allocation Problems." Journal of the Operational Research Society 48, no. 7 (July 1997): 745. http://dx.doi.org/10.2307/3010063.

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45

Gelain, Mirco, Maria Pini, Francesca Rossi, K. Venable, and Toby Walsh. "Local Search Approaches in Stable Matching Problems." Algorithms 6, no. 4 (October 3, 2013): 591–617. http://dx.doi.org/10.3390/a6040591.

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46

McDonald, Steve, and Tari Turner. "Big problems in search of small gains." Medical Journal of Australia 202, no. 7 (April 2015): 350. http://dx.doi.org/10.5694/mja15.00320.

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47

Gupta, D. K. "Tabu Search for Vehicle Routing Problems (VRPs)." International Journal of Computer Mathematics 79, no. 6 (January 2002): 693–701. http://dx.doi.org/10.1080/00207160211291.

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48

Vasconcelos, J. A., J. H. R. D. Maciel, and R. O. Parreiras. "Scatter search techniques applied to electromagnetic problems." IEEE Transactions on Magnetics 41, no. 5 (May 2005): 1804–7. http://dx.doi.org/10.1109/tmag.2005.846474.

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49

King, Jonathan L. "Three Problems in Search of a Measure." American Mathematical Monthly 101, no. 7 (August 1994): 609. http://dx.doi.org/10.2307/2974690.

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50

Yu, C. F., and B. W. Wah. "Learning dominance relations in combined search problems." IEEE Transactions on Software Engineering 14, no. 8 (1988): 1155–75. http://dx.doi.org/10.1109/32.7626.

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