Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
Papers published on a yearly basis
Papers
More filters
01 Dec 2000
TL;DR: In using the D-optimality criterion to minimize the workpiece positioning errors, two different greedy algorithms are developed for force-closure fixturing in the point set domain.
Abstract: Addresses the problem of fixture synthesis for 3-D workpieces with a set of discrete locations on the workpiece surface as a point set of candidates for locator and clamp placement. A sequential optimization approach is presented in order to reduce the complexity associated with an exhaustive search. The approach is based on a concept of optimum experimental design, while the optimization focuses on the fixture performance of workpiece localization accuracy. In using the D-optimality criterion to minimize the workpiece positioning errors, two different greedy algorithms are developed for force-closure fixturing in the point set domain. Both 2-D and 3-D examples are presented to illustrate the effectiveness of the synthesis approach.
125 citations
TL;DR: This paper proposes a greedy algorithm and provides a heuristic, based on regular cycles for all but one activity type, with a guaranteed worse case bound, and investigates properties of an optimal solution and shows that there is always a cyclic optimal policy.
124 citations
19 Jun 2004
TL;DR: In this article, the authors propose a general framework that embeds biclustering methods as local search procedures in an evolutionary algorithm and demonstrate on one prominent example that this approach achieves significant improvements in the quality of the biclusters when compared to the application of the greedy strategy alone.
Abstract: In recent years, several biclustering methods have been suggested to identify local patterns in gene expression data. Most of these algorithms represent greedy strategies that are heuristic in nature: an approximate solutions is found within reasonable time bounds. The quality of biclustering, though, is often considered more important than the computation time required to generate it. Therefore, this paper addresses the question whether additional run-time resources can be exploited in order to improve the outcome of the aforementioned greedy algorithms. To this end, we propose a general framework that embed such biclustering methods as local search procedures in an evolutionary algorithm. We demonstrate on one prominent example that this approach achieves significant improvements in the quality of the biclusters when compared to the application of the greedy strategy alone.
124 citations
16 May 1999
TL;DR: This paper discusses the performance of three different algorithms developed to solve the BS location problem: the greedy algorithm (GR), the genetic algorithm (GA) and the combination algorithm for total optimisation (CAT).
Abstract: The cost and complexity of a network is closely related to the number of base-stations (BSs) required to achieve the system operator's service objectives. The location of BSs is not an easy task and there are numerous factors that must be taken into account when deciding the optimum position of BSs. This paper discusses the performance of three different algorithms developed to solve the BS location problem: the greedy algorithm (GR), the genetic algorithm (GA) and the combination algorithm for total optimisation (CAT). These three methods are compared and results are given for a typical test scenario.
124 citations
24 Oct 1992
TL;DR: The authors derive matching upper and lower bounds for the competitive ratio of the on-line greedy algorithm for this problem, namely /sup (3n)2/3///sub 2/(1+o(1)), and derive a lower bound, Omega ( square root n), for any other deterministic or randomized on- line algorithm.
Abstract: The setup for the authors' problem consists of n servers that must complete a set of tasks. Each task can be handled only by a subset of the servers, requires a different level of service, and once assigned can not be re-assigned. They make the natural assumption that the level of service is known at arrival time, but that the duration of service is not. The on-line load balancing problem is to assign each task to an appropriate server in such a way that the maximum load on the servers is minimized. The authors derive matching upper and lower bounds for the competitive ratio of the on-line greedy algorithm for this problem, namely /sup (3n)2/3///sub 2/(1+o(1)), and derive a lower bound, Omega ( square root n), for any other deterministic or randomized on-line algorithm. >
123 citations