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Greedy Heuristics and an Evolutionary Algorithm for the BoundedDiameter Minimum Spanning Tree Problem
 Proceedings of the 2003 ACM Symposium on Applied Computing
, 2003
"... bound D, the boundeddiameter minimum spanning tree problem seeks a spanning tree on G of lowest weight in which no path between two vertices contains more than D edges. This problem is NPhard for 4 1, where n is the number of vertices in G. An existing greedy heuristic for the problem, called ..."
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Cited by 35 (13 self)
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bound D, the boundeddiameter minimum spanning tree problem seeks a spanning tree on G of lowest weight in which no path between two vertices contains more than D edges. This problem is NPhard for 4 1, where n is the number of vertices in G. An existing greedy heuristic for the problem, called OTTC, is based on Prim's algorithm. OTTC usually yields poor results on instances in which the triangle inequality approximately holds; it always uses the lowestweight edges that it can, but such edges do not in general connect the interior nodes of lowweight boundeddiameter trees. A new randomized greedy heuristic builds a boundeddiameter spanning tree from its center vertex or vertices. It chooses each next vertex at random but attaches the vertex with the lowestweight eligible edge. This algorithm is faster than OTTC and yields substantially better solutions on Euclidean instances. An evolutionary algorithm encodes spanning trees as lists of their edges, augmented with their center vertices. It applies operators that maintain the diameter bound and always generate valid o#spring trees. These operators are e#cient, so the algorithm scales well to larger problem instances. On 25 Euclidean instances of up to 1 000 vertices, the EA improved substantially on solutions found by the randomized greedy heuristic.
A Proactive Approach to Reconstructing Overlay Multicast Trees
, 2004
"... Overlay multicast constructs a multicast delivery tree among end hosts. Unlike traditional IP multicast, the nonleaf nodes in the tree are normal end hosts, which are potentially more susceptible to failures than routers and may leave the multicast group voluntarily. In these cases, all downstream n ..."
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Cited by 28 (1 self)
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Overlay multicast constructs a multicast delivery tree among end hosts. Unlike traditional IP multicast, the nonleaf nodes in the tree are normal end hosts, which are potentially more susceptible to failures than routers and may leave the multicast group voluntarily. In these cases, all downstream nodes will be affected. Thus an important problem in overlay multicast is how to recover from node departures in order to minimize the disruption of service to those affected nodes. In this paper, we propose a proactive approach to restore overlay multicast trees. Rather than letting downstream nodes try to find a new parent after a node departure, each nonleaf node precalculates a parenttobe for each of its children. When this nonleaf node is gone, all its children can find their respective new parents immediately. The salient feature of the approach is that each nonleaf node can compute a rescue plan for its children independently, and in most cases, rescue plans from multiple nonleaf nodes can work together for their children when they fail or leave at the same time. We develop a protocol for nodes to communicate with new parents so that the delivery tree can be quickly restored. Extensive simulations demonstrate that our proactive approach can recover from node departures 5 times faster than reactive methods in some cases, and 2 times faster on average.
EdgeSets: An Effective Evolutionary Coding of Spanning Trees
, 2002
"... The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we d ..."
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Cited by 14 (7 self)
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The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we describe initialization, recombination, and mutation operators for this representation. The operators offer
Peertopeer multipoint videoconferencing on the Internet Abstract
, 2005
"... A peertopeer architecture for multipoint videoconferencing is presented. Each conference participant may have asymmetric and dissimilar bandwidth connections to the Internet. The solution does not require additional hardware, as in multipoint control units, or network infrastructure support such a ..."
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Cited by 12 (2 self)
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A peertopeer architecture for multipoint videoconferencing is presented. Each conference participant may have asymmetric and dissimilar bandwidth connections to the Internet. The solution does not require additional hardware, as in multipoint control units, or network infrastructure support such as multicast. Without creating any additional demand on the networking and computing resources needed for a pointtopoint videoconference, this architecture can extend it into a multipoint one. A protocol for a completely distributed implementation has been developed and tested on a prototype system extending a pointtopoint video phone to a multipoint one. The architecture of the prototype system along with the details of the protocol optimization is discussed. Several performance results are presented.
Asymmetric information distances for automated taxonomy construction
 Knowledge and Information Systems
, 2009
"... A. Keyword distances The key requirement for stage one is a method of evaluating the similarity or distance between two areas of research, represented by appropriate keyword pairs. Existing studies have used methods such as citation analysis [Saka and Igami, 2007], [Small, 2006] and author/affiliati ..."
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Cited by 9 (5 self)
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A. Keyword distances The key requirement for stage one is a method of evaluating the similarity or distance between two areas of research, represented by appropriate keyword pairs. Existing studies have used methods such as citation analysis [Saka and Igami, 2007], [Small, 2006] and author/affiliationbased collaboration patterns [Zhu and Porter, 2002], [Anuradha et al., 2007] to extract the relationships between researchers and research topics. However, these approaches only utilize information from a limited number of publications at a time, and often require that the text of relevant publications be stored locally (see [Zhu and Porter, 2002], for example). As such, extending their use to massive collections of hundreds of thousands or millions of documents would be computationally unfeasible. Instead, we choose to explore an alternative approach which is to define the relationship between research areas in terms of the
A Comparison of Encodings and Algorithms for Multiobjective Minimum Spanning Tree Problems
 In Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01
, 1997
"... this paper we apply (appropriately modified) the best of recent methods for the (degreeconstrained) single objective MST problem to the multiobjective MST problem, and compare with a method based on Zhou and Gen's approach. Our evolutionary computation approaches, using the different encodings, inv ..."
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Cited by 8 (1 self)
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this paper we apply (appropriately modified) the best of recent methods for the (degreeconstrained) single objective MST problem to the multiobjective MST problem, and compare with a method based on Zhou and Gen's approach. Our evolutionary computation approaches, using the different encodings, involve a new populationbased variant of Knowles and Corne's PAES algorithm. We find the direct encoding to considerably outperform the Prufer encoding. And we find that a simple iterated approach, based on Prim's algorithm modified for the multiobjective MST, also significantly outperforms the Prufer encoding.
Evolution Strategies, Network Random Keys, and the OneMax Tree Problem
 In Applications of Evolutionary Computing: EvoWorkshops, Edited by Stefano Cagnoni, Jens
, 2002
"... Evolution strategies (ES) are efficient optimization methods for continuous problems. However, many combinatorial optimization methods can not be represented by using continuous representations. The development of the network random key representation which represents trees by using real numbers ..."
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Cited by 5 (0 self)
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Evolution strategies (ES) are efficient optimization methods for continuous problems. However, many combinatorial optimization methods can not be represented by using continuous representations. The development of the network random key representation which represents trees by using real numbers allows one to use ES for combinatorial tree problems.
A memetic algorithm for minimumcost vertexbiconnectivity augmentation of graphs
 JOURNAL OF HEURISTICS
, 2003
"... This paper considers the problem of augmenting a given graph by a cheapest possible set of additional edges in order to make the graph vertexbiconnected. A realworld instance of this problem is the enhancement of an already established computer network to become robust against single node failure ..."
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Cited by 4 (1 self)
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This paper considers the problem of augmenting a given graph by a cheapest possible set of additional edges in order to make the graph vertexbiconnected. A realworld instance of this problem is the enhancement of an already established computer network to become robust against single node failures. The presented memetic algorithm includes effective preprocessing of problem data and a fast local improvement strategy which is applied before a solution is included into the population. In this way, the memetic algorithm’s population consists always of only feasible, locally optimal solution candidates. Empirical results on two sets of test instances indicate the superiority of the new approach over two previous heuristics and an earlier genetic algorithm.
A Comparative Assessment of Memetic, Evolutionary, and Constructive Algorithms for the Multiobjective dMST Problem
 Proc. of 2001 Genetic and Evolutionary Computation Conference Workshop Program
, 1997
"... Finding a minimumweight spanning tree ..."
Codings and operators in two genetic algorithms for the leafconstrained minimum spanning tree problem
 International Journal of Applied Mathematics and Computer Science
, 2004
"... The features of an evolutionary algorithm that most determine its performance are the coding by which its chromosomes represent candidate solutions to its target problem and the operators that act on that coding. Also, when a problem involves constraints, a coding that represents only valid solution ..."
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Cited by 2 (1 self)
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The features of an evolutionary algorithm that most determine its performance are the coding by which its chromosomes represent candidate solutions to its target problem and the operators that act on that coding. Also, when a problem involves constraints, a coding that represents only valid solutions and operators that preserve that validity represent a smaller search space and result in a more effective search. Two genetic algorithms for the leafconstrained minimum spanning tree problem illustrate these observations. Given a connected, weighted, undirected graph G with n vertices and a bound ℓ, this problem seeks a spanning tree on G with at least ℓ leaves and minimum weight among all such trees. A greedy heuristic for the problem begins with an unconstrained minimum spanning tree on G, then economically turns interior vertices into leaves until their number reaches ℓ. One genetic algorithm encodes candidate trees with Prüfer strings decoded via the Blob Code. The second GA uses strings of length n−ℓ that specify trees ’ interior vertices. Both GAs apply operators that generate only valid chromosomes. The latter represents and searches a much smaller space. In tests on 65 instances of the problem, both Euclidean and with weights chosen randomly, the BlobCoded GA cannot compete with the greedy heuristic, but the subsetcoded GA consistently identifies leafconstrained spanning trees of lower weight than the greedy heuristic does, particularly on the random instances.