Results 1 - 10 of 2,243

Ant algorithms for discrete optimization

by Marco Dorigo, Gianni Di Caro, Luca M. Gambardella - ARTIFICIAL LIFE , 1999
"... This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic ..."
Abstract - Cited by 489 (42 self) - Add to MetaCart
This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic

Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions

by Alexander Strehl, Joydeep Ghosh, Claire Cardie - Journal of Machine Learning Research , 2002
"... This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
Abstract - Cited by 603 (20 self) - Add to MetaCart
' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners

Greedy Randomized Adaptive Search Procedures

by Mauricio G. C. Resende , Celso C. Ribeiro , 2002
"... GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract - Cited by 647 (82 self) - Add to MetaCart
GRASP is a multi-start metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search

Graphical models, exponential families, and variational inference

by Martin J. Wainwright, Michael I. Jordan , 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract - Cited by 819 (28 self) - Add to MetaCart
fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes

Tabu Search -- Part I

by Fred Glover , 1989
"... This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more ..."
Abstract - Cited by 680 (11 self) - Add to MetaCart
This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning

Metaheuristics in combinatorial optimization: Overview and conceptual comparison

by Christian Blum, Andrea Roli - ACM COMPUTING SURVEYS , 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
Abstract - Cited by 314 (17 self) - Add to MetaCart
The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important

Graph Cuts and Efficient N-D Image Segmentation

by Yuri Boykov, Gareth Funka-Lea , 2006
"... Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features ..."
Abstract - Cited by 307 (7 self) - Add to MetaCart
present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea

A general approximation technique for constrained forest problems

by Michel X. Goemans, David P. Williamson - SIAM J. COMPUT. , 1995
"... We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization proble ..."
Abstract - Cited by 414 (21 self) - Add to MetaCart
We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization

FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO Artificial Intelligence

by Fred Glover , 1986
"... Scope and Purpose-A summary is provided of some of the recent (and a few not-so-recent) developments that otTer promise for enhancing our ability to solve combinatorial optimization problems. These developments may be usefully viewed as a synthesis of the perspectives of operations research and arti ..."
Abstract - Cited by 379 (8 self) - Add to MetaCart
Scope and Purpose-A summary is provided of some of the recent (and a few not-so-recent) developments that otTer promise for enhancing our ability to solve combinatorial optimization problems. These developments may be usefully viewed as a synthesis of the perspectives of operations research

Bidding and Allocation in Combinatorial Auctions

by Noam Nisan - IN ACM CONFERENCE ON ELECTRONIC COMMERCE , 2000
"... When an auction of multiple items is performed, it is often desirable to allow bids on combinations of items, as opposed to only on single items. Such an auction is often called "combinatorial", and the exponential number of possible combinations results in computational intractability o ..."
Abstract - Cited by 275 (11 self) - Add to MetaCart
When an auction of multiple items is performed, it is often desirable to allow bids on combinations of items, as opposed to only on single items. Such an auction is often called "combinatorial", and the exponential number of possible combinations results in computational intractability
Results 1 - 10 of 2,243