Relevant bibliographies by topics / Greedy search algorithm

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Academic literature on the topic 'Greedy search algorithm'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Greedy search algorithm"

1

Stern, Roni, Rami Puzis, and Ariel Felner. "Potential Search: A New Greedy Anytime Heuristic Search." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (2010): 119–20. http://dx.doi.org/10.1609/socs.v1i1.18177.

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In this paper we explore a novel approach for anytime heuristic search, in which the node that is most probable to improve the incumbent solution is expanded first. This is especially suited for the "anytime aspect" of anytime algorithms - the possibility that the algorithm will be be halted anytime throughout the search. The potential of a node to improve the incumbent solution is estimated by a custom cost function, resulting in Potential Search, an anytime best-first search. Experimental results on the 15-puzzle and on the key player problem in communication networks (KPP-COM) show that this approach is competitive with state-of-the-art anytime heuristic search algorithms, and is more robust.
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2

Benabbou, Nawal, Cassandre Leroy, Thibaut Lust, and Patrice Perny. "Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12233–40. http://dx.doi.org/10.1609/aaai.v35i14.17452.

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We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or near-optimal base of a matroid. We also propose an interactive local search algorithm based on sequences of possibly improving exchanges for the same problem. For both algorithms, we provide performance guarantees on the quality of the returned solutions and the number of queries. Our algorithms are tested on the uniform, graphical and scheduling matroids to solve three different problems (committee election, spanning tree, and scheduling problems) and evaluated in terms of computation times, number of queries, and empirical error.
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3

Dor, Avner. "The Greedy Search Algorithm on Binary Vectors." Journal of Algorithms 27, no. 1 (1998): 42–60. http://dx.doi.org/10.1006/jagm.1997.0893.

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4

Prihozhy, A. A. "Exact and greedy algorithms of allocating experts to maximum set of programmer teams." «System analysis and applied information science», no. 1 (June 8, 2022): 40–46. http://dx.doi.org/10.21122/2309-4923-2022-1-40-46.

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The allocation of experts to programmer teams, which meet constraints on professional competences related to programming technologies, languages and tools an IT project specifies is a hard combinatorial problem. This paper solves the problem of forming the maximum number of teams whose experts meet all the constraints within each team. It develops and compares two algorithms: a heuristic greedy and exact optimal. The greedy algorithm iteratively solves the set cover problem on a matrix of expert competences until can create the next workable team of remaining experts. The paper proves that the allocation greedy algorithm is not accurate even if the set cover algorithm is exact. We call the allocation algorithm as double greedy if the set cover algorithm is greedy. The exact algorithm we propose finds optimal solution in three steps: generating a set of all non-redundant teams, producing a graph of team’s independency, and searching for a maximum clique in the graph. The algorithm of generating the non-redundant teams traverses a search tree constructed in such a way as to guarantee the creation of all non-redundant teams and absorbing all redundant teams. The edges of the non-redundant team independency graph connect teams that have no common expert. The maximum clique search algorithm we propose accounts for the problem and graph features. Experimental results show that the exact algorithm is a reference one, and the double-greedy algorithm is very fast and can yield suboptimal solutions for large-size allocation problems.
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5

Heusner, Manuel, Thomas Keller, and Malte Helmert. "Understanding the Search Behaviour of Greedy Best-First Search." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (2021): 47–55. http://dx.doi.org/10.1609/socs.v8i1.18425.

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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 behaviour 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 not expanded under any gbfs tie-breaking strategy. For the remaining states, we show that some are expanded by all gbfs searches, while others are expanded only if certain conditions are met.
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Piacentini, Chiara, Sara Bernardini, and J. Christopher Beck. "Autonomous Target Search with Multiple Coordinated UAVs." Journal of Artificial Intelligence Research 65 (August 8, 2019): 519–68. http://dx.doi.org/10.1613/jair.1.11635.

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Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.
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7

Kazakovtsev, Lev, Dmitry Stashkov, Mikhail Gudyma, and Vladimir Kazakovtsev. "Algorithms with greedy heuristic procedures for mixture probability distribution separation." Yugoslav Journal of Operations Research 29, no. 1 (2019): 51–67. http://dx.doi.org/10.2298/yjor171107030k.

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For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighbourhoods to search for better solutions. The results of computational experiments made on classical data sets and the testings of production batches of semiconductor devices shipped for the space industry demonstrate that new algorithms allow us to obtain better results, higher values of the log likelihood objective function, in comparison with the EM algorithm and its modifications.
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8

Xiao, Zhuolei, Yerong Zhang, Kaixuan Zhang, Dongxu Zhao, and Guan Gui. "GARLM: Greedy Autocorrelation Retrieval Levenberg–Marquardt Algorithm for Improving Sparse Phase Retrieval." Applied Sciences 8, no. 10 (2018): 1797. http://dx.doi.org/10.3390/app8101797.

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The goal of phase retrieval is to recover an unknown signal from the random measurements consisting of the magnitude of its Fourier transform. Due to the loss of the phase information, phase retrieval is considered as an ill-posed problem. Conventional greedy algorithms, e.g., greedy spare phase retrieval (GESPAR), were developed to solve this problem by using prior knowledge of the unknown signal. However, due to the defect of the Gauss–Newton method in the local convergence problem, especially when the residual is large, it is very difficult to use this method in GESPAR to efficiently solve the non-convex optimization problem. In order to improve the performance of the greedy algorithm, we propose an improved phase retrieval algorithm, which is called the greedy autocorrelation retrieval Levenberg–Marquardt (GARLM) algorithm. Specifically, the proposed GARLM algorithm is a local search iterative algorithm to recover the sparse signal from its Fourier transform magnitude. The proposed algorithm is preferred to existing greedy methods of phase retrieval, since at each iteration the problem of minimizing the objective function over a given support is solved by using the improved Levenberg–Marquardt (ILM) method and matrix transform. A local search procedure such as the 2-opt method is then invoked to get the optimal estimation. Simulation results are given to show that the proposed algorithm performs better than the conventional GESPAR algorithm.
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9

Oktaviandi, Rizky Berlia, M. Sadid Tafsirul Hadi, Alanfansyah Ghozy Santoso, and Nova El Maidah. "Perbandingan Algoritma Genetika dengan Algoritma Greedy Untuk Pencarian Rute Terpendek." INFORMAL: Informatics Journal 3, no. 1 (2019): 6. http://dx.doi.org/10.19184/isj.v3i1.9847.

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In everyday life we ​​often travel from place to place. So that we need to consider the time and cost efficient. Therefore, accuracy is needed to determine the shortest path as a consideration in decision to show the path to be taken. The results obtained also require speed and accuracy with the help of a computer. Using or functioning a computer there must be a distributed program in it. The programs contained in the computer vary widely and each program must use an algorithm. Algorithm is a collection of commands to solve a problem gradually from start to finish. There are various algorithms that can be used to find the shortest route such as Breadth First Search algorithm, Depth First Search, A *, Hill Climbing and others. For that required comparison algorithm which is able to find the shortest route accurately and efficiently. In this journal, the algorithm to be compared is the genetic algorithm and greedy algorithm to find the shortest route on a map. Some aspects to be compared are aspects of the accuracy, speed, and complexity of genetic algorithms and greedy algorithms for the shortest route search.
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10

Wang, Chao, Deguang Wang, and Chun Jin. "A quick Heuristic and a general search algorithm for traveling salesman problem." E3S Web of Conferences 360 (2022): 01097. http://dx.doi.org/10.1051/e3sconf/202236001097.

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This paper puts forward a constructive heuristic algorithm called the method of inserting the minimum neighbor edge from outside to the center (IMNEFOTC) that can be applied to solve large-scale and ultra-large-scale travelling salesman problems. Through it and the randomized greedy heuristic algorithm (RGH) which greedy heuristic algorithm is modified, a general meta-heuristic search algorithm framework is built. The general search algorithm (GSA) is based on a set of initial solutions, and continuous 2-opt operations are performed, so as to search for solutions in better quality. The data from the experiment reveals that the performance of IMNEFOTC is better than the traditional greedy algorithm, and the GSA can obtain a pretty satisfactory solution in a reasonable range of time.
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