Relevant bibliographies by topics / Search algorithms

Academic literature on the topic 'Search algorithms'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Search algorithms"

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Siddique, Nazmul, and Hojjat Adeli. "Hybrid Harmony Search Algorithms." International Journal on Artificial Intelligence Tools 24, no. 06 (December 2015): 1530001. http://dx.doi.org/10.1142/s021821301530001x.

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Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.
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Aine, Sandip, and Maxim Likhachev. "Search Portfolio with Sharing." Proceedings of the International Conference on Automated Planning and Scheduling 26 (March 30, 2016): 11–19. http://dx.doi.org/10.1609/icaps.v26i1.13760.

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Over the years, a number of search algorithms have been proposed in AI literature, ranging from best-first to depth-first searches, from incomplete to optimal searches, from linear memory to unbounded memory searches; each having their strengths and weaknesses. The variability in performance of these algorithms makes algorithm selection a hard problem, especially for performance critical domains. Algorithm portfolios alleviate this problem by simultaneously running multiple algorithms to solve a given problem instance, exploiting their diversity. In general, the portfolio methods do not share information among candidate algorithms. Our work is based on the observation that if the algorithms within a portfolio can share information, it may significantly enhance the performance, as one algorithm can now utilize partial results computed by other algorithms. To this end, we introduce a new search framework, called Search Portfolio with Sharing (SP-S), which uses multiple algorithms to explore a given state-space in an integrated manner, seamlessly combining the partial solutions, while preserving the constraints/characteristics of the candidate algorithms. In addition, SP-S can be easily adopted to guarantee theoretical properties like completeness, bounded sub-optimality, and bounded re-expansions. We describe the basics of the SP-S framework and explain how different classes of search algorithms can be integrated in SP-S. We discuss its theoretical properties and present experimental results for multiple domains, demonstrating the utility of such a shared approach.
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KOREPIN, VLADIMIR E., and YING XU. "QUANTUM SEARCH ALGORITHMS." International Journal of Modern Physics B 23, no. 31 (December 20, 2009): 5727–58. http://dx.doi.org/10.1142/s0217979209054922.

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This article reviews recent progress in quantum database search algorithms. The subject is presented in a self-contained and pedagogical way. The problem of searching a large database (a Hilbert space) for a target item is performed by the famous Grover algorithm which locates the target item with high probability and a quadratic speed-up compared with the corresponding classical algorithm. If the database is partitioned into blocks and one is searching for the block containing the target item instead of the target item itself, then the problem is referred to as partial search. Partial search trades accuracy for speed and the most efficient version is the Grover–Radhakrishnan–Korepin (GRK) algorithm. The target block can be further partitioned into sub-blocks so that GRK's can be performed in a sequence called a hierarchy. We study the Grover search and GRK partial search in detail and prove that a GRK hierarchy is less efficient than a direct GRK partial search. Both the Grover search and the GRK partial search can be generalized to the case with several target items (or target blocks for a GRK). The GRK partial search algorithm can also be represented in terms of group theory.
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Ambainis, A. "Quantum search algorithms." ACM SIGACT News 35, no. 2 (June 2004): 22–35. http://dx.doi.org/10.1145/992287.992296.

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Yan, Shaoqiang, Weidong Liu, Xinqi Li, Ping Yang, Fengxuan Wu, and Zhe Yan. "Comparative Study and Improvement Analysis of Sparrow Search Algorithm." Wireless Communications and Mobile Computing 2022 (August 31, 2022): 1–15. http://dx.doi.org/10.1155/2022/4882521.

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To solve the problem that the emerging sparrow search algorithm (SSA) lacks systematic comparison and analysis with other classical algorithms, this paper first introduces the principle of the sparrow search algorithm and then describes the mathematical model and algorithm description of the sparrow search algorithm. By comparing several classical intelligent algorithms with particle swarm optimization (PSO), differential evolution (DE), and gray wolf optimizer (GWO), the sparrow search algorithm’s theory and model are systematically compared and analyzed, and the advantages and disadvantages of SSA are summarized. Finally, based on the above research and previous research, the limitations of SSA and current improved SSA are analyzed, which provides ideas for further improvement of the algorithm.
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Tobing, Fenina Adline Twince, and Rena Nainggolan. "ANALISIS PERBANDINGAN PENGGUNAAN METODE BINARY SEARCH DENGAN REGULAR SEARCH EXPRESSION." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 4, no. 2 (October 31, 2021): 168–72. http://dx.doi.org/10.46880/jmika.vol4no2.pp168-172.

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The search system is a feature that is indispensable for an application or website. By comparing two algorithms that are often used, namely Binary Search and Regular Search Expression (REGEX) algorithms in a simple search system is a problem that will be discussed in this journal. Analysis of the two algorithms is carried out to solve problems in the search system so that the search algorithm can be applied more precisely and effectively. The results prove that the Binary Search has the advantage of searching large amounts of data in an ordered state and has a more effective iteration. While the Regular Expression Search has the advantage of performing searches that are not completely known about the results and keys, besides that this algorithm also allows you to search based on certain patterns in the data.
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Chen, Jingwei, Nathan Sturtevant, William Doyle, and Wheeler Ruml. "Revisiting Suboptimal Search." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (September 1, 2021): 18–25. http://dx.doi.org/10.1609/socs.v10i1.18498.

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Suboptimal search algorithms can often solve much larger problems than optimal search algorithms, and thus have broad practical use. This paper returns to early algorithms like WA*, A*_e and Optimistic search. It studies the commonalities between these approaches in order to build a new bounded-suboptimal algorithm. Combined with recent research on avoiding node re-expansions in bounded-optimal search, a new solution quality bound is developed, which often provides proof of the solution bound much earlier during the search. Put together, these ideas provide a new state-of-the-art in bounded-optimal search.
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Soongsathitanon, S., W. L. Woo, and S. S. Dlay. "Fast search algorithms for video coding using orthogonal logarithmic search algorithm." IEEE Transactions on Consumer Electronics 51, no. 2 (May 2005): 552–59. http://dx.doi.org/10.1109/tce.2005.1468001.

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Huang, J., and A. Darwiche. "The Language of Search." Journal of Artificial Intelligence Research 29 (June 24, 2007): 191–219. http://dx.doi.org/10.1613/jair.2097.

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This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can be interpreted as a combinational circuit, and a search algorithm then defines a propositional language consisting of circuits that are generated across all possible executions of the algorithm. In particular, we show that several versions of exhaustive DPLL search correspond to such well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF. By thus mapping search algorithms to propositional languages, we provide a uniform and practical framework in which successful search techniques can be harnessed for compilation of knowledge into various languages of interest, and a new methodology whereby the power and limitations of search algorithms can be understood by looking up the tractability and succinctness of the corresponding propositional languages.
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SHAH, I. "DIRECT ALGORITHMS FOR FINDING MINIMAL UNSATISFIABLE SUBSETS IN OVER-CONSTRAINED CSPs." International Journal on Artificial Intelligence Tools 20, no. 01 (February 2011): 53–91. http://dx.doi.org/10.1142/s0218213011000036.

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In many situations, an explanation of the reasons behind inconsistency in an overconstrained CSP is required. This explanation can be given in terms of minimal unsatisfiable subsets (MUSes) of constraints. This paper presents algorithms for finding minimal unsatisfiable subsets (MUSes) of constraints in overconstrained CSPs with finite domains and binary constraints. The approach followed is to generate subsets in the subset space, test them for consistency and record the inconsistent subsets found. We present three algorithms as variations of this basic approach. Each algorithm generates subsets in the subset space in a different order and curtails search by employing various search pruning mechanisms. The proposed algorithms are anytime algorithms: a time limit can be set on an algorithm's search and the algorithm can be made to find a subset of MUSes. Experimental evaluation of the proposed algorithms demonstrates that they perform two to three orders of magnitude better than the existing indirect algorithms. Furthermore, the algorithms are able to find MUSes in large CSP benchmarks.
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Dissertations / Theses on the topic "Search algorithms"

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Hein, Birgit. "Quantum search algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11512/.

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In this thesis two quantum search algorithms on two different graphs, a hypercube and a d-dimensional square lattice, are analysed and some applications of the lattice search are discussed. The approach in this thesis generalises a picture drawn by Shenvi, Kempe and Whaley, which was later adapted by Ambainis, Kempe and Rivosh. It defines a one parameter family of unitary operators U_λ with parameter λ. It will be shown that two eigenvalues of U_λ form an avoided crossing at the λ-value where U_λ is equal to the old search operator. This generalised picture opens the way for a construction of two approximate eigen- vectors at the crossing and gives rise to a 2×2 model Hamiltonian that is used to approximate the operator U_λ near the crossing. The thus defined Hamiltonian can be used to calculate the leading order of search time and success probability for the search. To the best of my knowledge only the scaling of these quantities has been known. For the algorithm searching the regular lattice, a generalisation of the model Hamiltonian for m target vertices is constructed. This algorithm can be used to send a signal from one vertex of the graph to a set of vertices. The signal is transmitted between these vertices exclusively and is localised only on the sender and the receiving vertices while the probability to measure the signal at one of the remaining vertices is significantly smaller. However, this effect can be used to introduce an additional sender to search settings and send a continuous signal to all target vertices where the signal will localise. This effect is an improvement compared to the original search algorithm as it does not need to know the number of target vertices.
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Dow, P. Alex. "Search algorithms for exact treewidth." Diss., Restricted to subscribing institutions, 2010. http://proquest.umi.com/pqdweb?did=2023774451&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Gambardella, Luca Maria. "Coupling ant colony system with local search." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209045.

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In the last decades there has been a lot of interest in computational models and metaheuristics algorithms capable to solve combinatorial optimization problems. The recent trend is to define these algorithms taking inspiration by the observation of natural systems. In this thesis the Ant Colony System (ACS) is presented which has been inspired by the observation of real ant colonies. ACS is initially proposed to solve the symmetric and asymmetric travelling salesman problems where it is shown to be competitive with other metaheuristics. Although this is an interesting and promising result, it was immediately clear that ACS, as well as other metaheuristics, in many cases cannot compete with specialized local search methods. An interesting trend is therefore to couple metaheuristics with a local optimizer, giving birth to so-called hybrid methods. Along this line, the thesis investigates MACS-VRPTW (Multiple ACS for the Vehicle Routing Problem with Time Windows) and HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem (SOP). In the second part the thesis introduces some modifications of the original ACS algorithm. These modifications are able to speed up the method and to make it more competitive in case of large problem instances. The resulting framework, called Enhanced Ant Colony System is tested for the SOP. Finally the thesis presents the application of ACS to solve real-life vehicle routing problems where additional constraints and stochastic information are included.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
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Trippen, Gerhard Wolfgang. "Online exploration and search in graphs /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20TRIPPE.

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Kibriya, Ashraf Masood. "Fast Algorithms for Nearest Neighbour Search." The University of Waikato, 2007. http://hdl.handle.net/10289/2463.

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The nearest neighbour problem is of practical significance in a number of fields. Often we are interested in finding an object near to a given query object. The problem is old, and a large number of solutions have been proposed for it in the literature. However, it remains the case that even the most popular of the techniques proposed for its solution have not been compared against each other. Also, many techniques, including the old and popular ones, can be implemented in a number of ways, and often the different implementations of a technique have not been thoroughly compared either. This research presents a detailed investigation of different implementations of two popular nearest neighbour search data structures, KDTrees and Metric Trees, and compares the different implementations of each of the two structures against each other. The best implementations of these structures are then compared against each other and against two other techniques, Annulus Method and Cover Trees. Annulus Method is an old technique that was rediscovered during the research for this thesis. Cover Trees are one of the most novel and promising data structures for nearest neighbour search that have been proposed in the literature.
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Wong, Brian Wai Fung. "Deep-web search engine ranking algorithms." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61246.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-80).
The deep web refers to content that is hidden behind HTML forms. The deep web contains a large collection of data that are unreachable by link-based search engines. A study conducted at University of California, Berkeley estimated that the deep web consists of around 91,000 terabytes of data, whereas the surface web is only about 167 terabytes. To access this content, one must submit valid input values to the HTML form. Several researchers have studied methods for crawling deep web content. One of the most promising methods uses unique wrappers for HTML forms. User inputs are first filtered through the wrappers before being submitted to the forms. However, this method requires a new algorithm for ranking search results generated by the wrappers. In this paper, I explore methods for ranking search results returned from a wrapped-based deep web search engine.
by Brian Wai Fung Wong.
M.Eng.
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Yu, Jenn-Hwa. "Probabilistic analysis of some search algorithms /." The Ohio State University, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487683756126241.

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Orr, Genevieve Beth. "Dynamics and algorithms for stochastic search /." Full text open access at:, 1995. http://content.ohsu.edu/u?/etd,197.

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Ganai, Malay Kumar. "Algorithms for efficient state space search /." Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008331.

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Kroyan, Julia. "Trust-search algorithms for unconstrained optimization /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3120456.

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Books on the topic "Search algorithms"

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Salander, Elisabeth C., and Elisabeth C. Salander. Computer search algorithms. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Salander, Elisabeth C. Computer search algorithms. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Algorithms 2: Data structures and search algorithms. Chichester: Wiley, 1990.

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Cicalese, Ferdinando. Fault-Tolerant Search Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-17327-1.

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Stefan, Schrödl, ed. Heuristic search: Theory and applications. Amsterdam: Morgan Kaufmann, 2011.

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Quantum walks and search algorithms. New York: Springer, 2013.

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Portugal, Renato. Quantum Walks and Search Algorithms. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97813-0.

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Portugal, Renato. Quantum Walks and Search Algorithms. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6336-8.

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Hamadi, Youssef. Combinatorial Search: From Algorithms to Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41482-4.

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Webb, Daniel James. Evaluation of spectral database search algorithms. Wolverhampton: University of Wolverhampton, 1997.

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Book chapters on the topic "Search algorithms"

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Sastry, Kumara, David E. Goldberg, and Graham Kendall. "Genetic Algorithms." In Search Methodologies, 93–117. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_4.

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Izadkhah, Habib. "Search." In Problems on Algorithms, 269–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17043-0_8.

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Karimov, Elshad. "Search Algorithms." In Data Structures and Algorithms in Swift, 155–61. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5769-2_15.

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Seidl, Thomas, and Jost Enderle. "Binary Search." In Algorithms Unplugged, 5–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15328-0_1.

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Elmasry, Amr, and Jyrki Katajainen. "Branchless Search Programs." In Experimental Algorithms, 127–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38527-8_13.

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Cygan, Marek, Fedor V. Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, and Saket Saurabh. "Bounded Search Trees." In Parameterized Algorithms, 51–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21275-3_3.

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Pătraşcu, Mihai. "Predecessor Search." In Encyclopedia of Algorithms, 1601–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_298.

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Patel, Apoorva D., and Lov K. Grover. "Quantum Search." In Encyclopedia of Algorithms, 1707–16. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_317.

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K.Grover, Lov, and Apoorva D.Patel. "Quantum Search." In Encyclopedia of Algorithms, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_317-2.

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Belazzougui, Djamal. "Predecessor Search." In Encyclopedia of Algorithms, 1–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_632-1.

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Conference papers on the topic "Search algorithms"

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Sormaz, Dusan N. "Agent-Based Process Sequencing Using Search Algorithms." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21071.

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Process sequencing represents one of very important tasks in the process planning. The order of tasks and the use of resources are determined by sequencing, and therefore the decision carries the burden of finally optimizing the whole process plan of the part. This paper proposes a flexible, agent-based framework for process sequencing which allows for realtime selection of the sequencing algorithm, dependent on the stage of the product development. The framework has been developed around a tool called space searcher which provides for application of space search algorithms in various domain. Space searcher receives a sequencing agent which provides the sequencing algorithm and executes a space search in order to generate context-specific optimal process sequence. Several process sequencing algorithms (and corresponding agents for space searcher) are described in detail. The application of those algorithms is illustrated on few examples.
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Mekhilef, Mounib, and Mohamed B. Trabia. "Successive Twinkling Simplex Search Optimization Algorithms." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dac-21132.

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Abstract Simplex algorithms have been proven to be a reliable nonlinear programming pattern search algorithm. The effectiveness of simplex however reduces when the solved problem has large number of variables, several local minima, or when initial guess is not readily available. Recent results obtained by introducing a technique of random selection of the variables in optimization processes, encouraged studying the effect of this idea on the Nelder & Mead version of the simplex algorithm to improve its semi-global behavior. This paper proposes several enhancements to the simplex. The algorithm is run for several attempts. Each attempt uses the final answer of the previous attempt as an initial guess. At each attempt, the search starts by generating a simplex with n+1 vertices. A random subset of the variables is involved in the movement (reflection, expansion, contraction, shrinking) of the simplex. This process is called twinkling. The paper presents several variations of twinkling the simplex. The random nature of the algorithm lends itself to problems with large dimensionality or complex topography. The algorithm is applied successfully to several engineering design problems. The results compare favorably with both regular simplex and genetic algorithms.
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Pargas, Roy P., Jennifer Ludwick, and Steven Spoon. "Hybrid search algorithms." In the 1997 ACM symposium. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/331697.331752.

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Magda, Enescu Florentina, Stirbu Cosmin, and Lita Adrian Ioan. "Image search algorithms." In 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2015. http://dx.doi.org/10.1109/ecai.2015.7301248.

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Shaham, Eshed, Ariel Felner, Nathan R. Sturtevant, and Jeffrey S. Rosenschein. "Optimally Efficient Bidirectional Search." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/867.

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A* is optimally efficient with regard to node expansions among unidirectional admissible algorithms — those that only assume that the heuristic used is admissible. This paper studies algorithms that are optimally efficient for bidirectional search algorithms. We present the Fractional MM algorithm and its sibling, the MT algorithm, which is simpler to analyze. We then develop variants of these algorithms that are optimally efficient, each under different assumptions on the information available to the algorithm.
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Hossain, Md Emran, Syed Bayazid Hossain, Md Sha Alam Tutul, and Shamsun Nahar. "Optimized Search Functionality with Linear Search Algorithm." In International Conference on Emerging Trends in Engineering and Advanced Science. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.123.6.

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Linear search is the essential search algorithm used in fact structures. If is likewise called sequential search. Linear search used to discover a specific detail in an array. It is today no longer obligatory to display an array in any order (Ascending or Descending) as in the case of binary search. Linear search initiates advanced with the support of using sequentially scanning the influences within the array and if the detail has been found, if will display the unique detail and the index value of that detail in that array. In this paper we contemporary a unique searching algorithm of modified Linear Search, which is an improved form of linear search algorithm and its created by likening the influences from each termination. We have additionally in comparison the new Linear search set of rules with Linear Search Algorithm. We used a system for implementation and Analysis of time taken with the aid of using each the algorithms. Result shows that Two Way Searching Algorithm is working glowing for all comments standards and it takes lesser time if the detail to be search is after the middle of the array, otherwise it takes same time as if of linear search. Remaining primarily based totally on the linear search and binary search algorithms; one algorithm designed to act on connected linear list.
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Amin, S. "Dynamic local search." In Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971168.

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Franz, V., and J. B. Anderson. "Reduced-search BCJR algorithms." In Proceedings of IEEE International Symposium on Information Theory. IEEE, 1997. http://dx.doi.org/10.1109/isit.1997.613145.

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Sukthanker, Rhea Sanjay, Zhiwu Huang, Suryansh Kumar, Erik Goron Endsjo, Yan Wu, and Luc Van Gool. "Neural Architecture Search of SPD Manifold Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/413.

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In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem with a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and traditional NAS algorithms. Empirical results show that our algorithm excels in discovering better performing SPD network design and provides models that are more than three times lighter than searched by the state-of-the-art NAS algorithms.
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Heusner, Manuel, Thomas Keller, and Malte Helmert. "Search Progress and Potentially Expanded States in Greedy Best-First Search." 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.2018/735.

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A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.
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Reports on the topic "Search algorithms"

1

Lomonaco, Samuel J., and Louis H. Kauffman. Search for New Quantum Algorithms. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada451747.

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Loveland, D. W. Search Algorithms and Their Implementation. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada170802.

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3

Howe, Adele, and L. D. Whitley. Model Justified Search Algorithms for Scheduling Under Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada492118.

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4

Schwartz, Richard, and Steve Austin. Efficient, High-Performance Algorithms for N-Best Search. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada460351.

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Brewer, Eric A., Chrysanthos N. Dellarocas, Adrian Colbrook, and William E. Weihl. Algorithms for Search Trees on Message-Passing Architectures. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada241344.

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Willis, Mark A. Biologically-Inspired Search Algorithms for Locating Unseen Odor Sources. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada402125.

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

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Abramson, Mark A., Charles Audet, Jr Dennis, and J. E. Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada445031.

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Jacobson, Sheldon H. Finite-Time Performance of Local Search Algorithms: Theory and Application. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada522073.

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10

Nguyen, Long, Richard Schwartz, Francis Kubala, and Paul Placeway. Search Algorithms for Software-Only Real-Time Recognition with Very Large Vocabularies. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada457473.

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