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Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.


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Journal ArticleDOI
01 Jul 2006
TL;DR: This paper shows that an approach based on simulated annealing is well suited to the problem of locating the most significant bicluster, and presents a comparative evaluation of simulatedAnnealing and node deletion on a variety of datasets.
Abstract: In a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Heuristic approaches such as Cheng and Church's greedy node deletion algorithm have been previously employed. It is to be expected that stochastic search techniques such as evolutionary algorithms or simulated annealing might improve upon such greedy techniques. In this paper we show that an approach based on simulated annealing is well suited to this problem, and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases. Furthermore, we also test the ability of our technique to locate biologically verifiable biclusters within an annotated set of genes

100 citations

Journal ArticleDOI
TL;DR: A genetic algorithm (GA) with dual-chromosome coding for CTSP is presented and the results suggest that SAGA can achieve the best quality of solutions and HCGA should be the choice making good tradeoff between the solution quality and computing time.
Abstract: The multiple traveling salesman problem (MTSP) is an important combinatorial optimization problem. It has been widely and successfully applied to the practical cases in which multiple traveling individuals (salesmen) share the common workspace (city set). However, it cannot represent some application problems where multiple traveling individuals not only have their own exclusive tasks but also share a group of tasks with each other. This work proposes a new MTSP called colored traveling salesman problem (CTSP) for handling such cases. Two types of city groups are defined, i.e., each group of exclusive cities of a single color for a salesman to visit and a group of shared cities of multiple colors allowing all salesmen to visit. Evidences show that CTSP is NP-hard and a multidepot MTSP and multiple single traveling salesman problems are its special cases. We present a genetic algorithm (GA) with dual-chromosome coding for CTSP and analyze the corresponding solution space. Then, GA is improved by incorporating greedy, hill-climbing (HC), and simulated annealing (SA) operations to achieve better performance. By experiments, the limitation of the exact solution method is revealed and the performance of the presented GAs is compared. The results suggest that SAGA can achieve the best quality of solutions and HCGA should be the choice making good tradeoff between the solution quality and computing time.

100 citations

Journal ArticleDOI
01 Jan 2002
TL;DR: This paper conducts an extensive computational study of 11 annealing-based heuristics for the traveling salesman problem, applying each heuristic to 29 traveling salesman problems taken from a well-known online library, and comparing the results with respect to accuracy and running time.
Abstract: Recently, several general optimization algorithms based on the demon algorithm from statistical physics have been developed and tested on a few traveling salesman problems with encouraging results. In this paper, we conduct an extensive computational study of 11 annealing-based heuristics for the traveling salesman problem. We code versions of simulated annealing, threshold accepting, record-to-record travel and eight heuristics based on the demon algorithm. We apply each heuristic to 29 traveling salesman problems taken from a well-known online library, compare the results with respect to accuracy and running time and provide insights and suggestions for future work.

99 citations

Journal ArticleDOI
TL;DR: A new greedy-like heuristic method is proposed, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1MDKP and significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions.
Abstract: In this paper, we propose a new greedy-like heuristic method, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1 MDKP. Our heuristic differs from the existing greedy-like heuristics in two aspects. First, existing heuristics rely on each item’s aggregate consumption of resources to make item selection decisions, whereas our heuristic uses the effective capacity, defined as the maximum number of copies of an item that can be accepted if the entire knapsack were to be used for that item alone, as the criterion to make item selection decisions. Second, other methods increment the value of each decision variable only by one unit, whereas our heuristic adds decision variables to the solution in batches and consequently improves computational efficiency significantly for large-scale problems. We demonstrate that the new heuristic significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions. The new heuristic proves especially efficient for high dimensional knapsack problems with small-to-moderate numbers of decision variables, usually considered as “hard” MDKP and no computationally efficient heuristic is available to treat such problems.

99 citations

Proceedings ArticleDOI
24 Oct 2008
TL;DR: This paper proposes efficient approximation algorithms that give near optimal solutions with provable analytical bounds in the spectrum allocation problem in cellular networks under the coordinated dynamic spectrum access (CDSA) model.
Abstract: In this paper, we address the spectrum allocation problem in cellular networks under the coordinated dynamic spectrum access (CDSA) model. In this model, a centralized spectrum broker owns a part of the spectrum and issues dynamic spectrum leases to competing base stations in the region it controls. We consider a dynamic auction based approach where the base stations bid for channels depending on their demands. The broker allocates channels to them with an objective to maximize the overall revenue generated subject to wireless interference in the network. This problem is known to be NP-hard and has been addressed before in limited context. We address this problem in a very generic context where (i) interference in the network is modeled using pairwise and physical interference models and (ii) base stations can bid for heterogeneous channels of different width using generic bidding functions. We propose efficient approximation algorithms that give near optimal solutions with provable analytical bounds. Detailed simulation studies using randomly generated and real base station networks show that our algorithms scale very well for large network sizes.

99 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023350
2022690
2021809
2020939
20191,006
2018967