Results 1  10
of
62
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by . . .
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
Abstract

Cited by 851 (14 self)
 Add to MetaCart
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximumlikelihood programs and much higher than the performance of distancebased and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximumlikelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distancebased and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/. [Algorithm; computer simulations; maximum likelihood; phylogeny; rbcL; RDPII project.] The size of homologous sequence data sets has increased dramatically in recent years, and many of these data sets now involve several hundreds of taxa. Moreover, current probabilist...
Very LargeScale Neighborhood Search for the Quadratic Assignment Problem
 DISCRETE APPLIED MATHEMATICS
, 2002
"... The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances ..."
Abstract

Cited by 108 (11 self)
 Add to MetaCart
The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances (typically, n < 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n²). Previous efforts to explore larger size neighborhoods (such as 3exchange or 4exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very largescale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.
Modelbased search for combinatorial optimization
, 2001
"... Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as ..."
Abstract

Cited by 45 (13 self)
 Add to MetaCart
Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms. 1
Planning by Rewriting
 Journal of Artificial Intelligence Research
, 2001
"... Domainindependent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient highquality domainindependent planning. PbR exploits declarative planrewriting rules ..."
Abstract

Cited by 34 (4 self)
 Add to MetaCart
Domainindependent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient highquality domainindependent planning. PbR exploits declarative planrewriting rules and efficient local search techniques to transform an easytogenerate, but possibly suboptimal, initial plan into a highquality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating highquality plans.
A very large scale neighborhood search algorithm for the quadratic assignment problem
 JOURNAL ON COMPUTING
, 2002
"... Many optimization problems of practical interest are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic (approximation) algorithms that can find nearly optimal solutions within a reasonable amount of computation time. An improvement algorith ..."
Abstract

Cited by 20 (3 self)
 Add to MetaCart
Many optimization problems of practical interest are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic (approximation) algorithms that can find nearly optimal solutions within a reasonable amount of computation time. An improvement algorithm generally starts with a feasible solution and iteratively tries to obtain a better solution. Neighborhood search algorithms (alternatively called local search algorithms) are a wide class of improvement heuristics where at each iteration an improving solution is found by searching the “neighborhood” of the current solution. A critical issue in the design of a neighborhood search approach is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. As a rule of thumb, the larger the neighborhood, the better is the quality of the locally optimal solutions, and the greater is the accuracy of the final solution that is obtained. At the same time, the larger the neighborhood, the longer it takes to search the neighborhood at each iteration. For this reason a larger neighborhood does not necessarily produce a more effective heuristic unless one can search the larger neighborhood in a very efficient manner. This paper concentrates on neighborhood search algorithms where the size of the neighborhood is “very large” with respect to the size of the input data and in which the neighborhood is searched in an efficient manner. We survey three broad classes of very large scale neighborhood search (VLSN) algorithms: (1) variable depth
From decision theory to decision aiding methodology (my very personal version of this history and some related reflections)
, 2003
"... ..."
Better Algorithms and Bounds for Directed Maximum Leaf Problems
 Lect. Notes Comput. Sci
, 2007
"... Abstract. The Directed Maximum Leaf OutBranching problem is to find an outbranching (i.e. a rooted oriented spanning tree) in a given digraph with the maximum number of leaves. In this paper, we improve known parameterized algorithms and combinatorial bounds on the number of leaves in outbranchin ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
Abstract. The Directed Maximum Leaf OutBranching problem is to find an outbranching (i.e. a rooted oriented spanning tree) in a given digraph with the maximum number of leaves. In this paper, we improve known parameterized algorithms and combinatorial bounds on the number of leaves in outbranchings. We show that – every strongly connected digraph D of order n with minimum indegree at least 3 has an outbranching with at least (n/4) 1/3 − 1 leaves; – if a strongly connected digraph D does not contain an outbranching with k leaves, then the pathwidth of its underlying graph is O(k log k); – it can be decided in time 2 O(k log2 k) · n O(1) whether a strongly connected digraph on n vertices has an outbranching with at least k leaves. All improvements use properties of extremal structures obtained after applying local search and properties of some outbranching decompositions. 1
Finding critical traffic matrices
 In Proceedings of DSN ’05
, 2005
"... A traffic matrix represents the amount of traffic between origin and destination in a network. It has tremendous potential utility for many IP network engineering applications, such as network survivability analysis, traffic engineering, and capacity planning. Recent advances in traffic matrix estim ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
A traffic matrix represents the amount of traffic between origin and destination in a network. It has tremendous potential utility for many IP network engineering applications, such as network survivability analysis, traffic engineering, and capacity planning. Recent advances in traffic matrix estimation have enabled ISPs to measure traffic matrices continuously. Yet a major challenge remains towards achieving the full potential of traffic matrices. In practical networking applications, it is often inconvenient (if not infeasible) to deal with hundreds or thousands of measured traffic matrices. So it is highly desirable to be able to extract a small number of “critical ” traffic matrices. Unfortunately, we are not aware of any good existing solutions to this problem (other than a few ad hoc heuristics). This seriously limits the applicability of traffic matrices. To bridge the gap between the measurement and the actual application of traffic matrices, we study the critical traffic matrices selection (CritMat) problem in this paper. We developed a mathematical problem formalization after identifying the key requirements and properties of CritMat in the context of network design and analysis. Our complexity analysis showed that CritMat is NPhard. We then developed several clusteringbased approximation algorithms to CritMat. We evaluated these algorithms using a large collection of real traffic matrices collected in AT&T’s North American backbone network. Our results demonstrated that these algorithms are very effective and that a small number (e.g., 12) of critical traffic matrices suffice to yield satisfactory performance. 1
Local++: A C++ Framework for Local Search Algorithms
 Softw. Pract. Exp
, 1999
"... Local search is an emerging paradigm for combinatorial search which has been recently shown to be very effective for a large number of combinatorial problems. It is based on the idea of navigating the search space by iteratively stepping from one solution to one of its neighbors, which are obtained ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
Local search is an emerging paradigm for combinatorial search which has been recently shown to be very effective for a large number of combinatorial problems. It is based on the idea of navigating the search space by iteratively stepping from one solution to one of its neighbors, which are obtained by applying a simple local change to it. In this paper we present Local++, an objectoriented framework to be used as a general tool for the development and the implementation of local search algorithms in C++. The framework comprises a hierarchy of abstract template classes, one for each local search technique taken into account (i.e., hillclimbing, simulated annealing, and tabu search). Each class specifies and implements the invariant part of the algorithm built according to the technique, and is supposed to be specialized by a concrete class once a given search problem is considered, so as to implement the problemdependent part of the algorithm. Local++ comprises also a se...