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Neural Optimization
 The Handbook of Brain Research and Neural Networks. Bradford Books/The
, 1998
"... Introduction Many combinatorial optimization problems require a more or less exhaustive search to achieve exact solutions, with the computational effort growing exponentially or worse with system size. Various kinds of heuristic methods are therefore often used to find reasonably good solutions. Th ..."
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Cited by 164 (7 self)
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Introduction Many combinatorial optimization problems require a more or less exhaustive search to achieve exact solutions, with the computational effort growing exponentially or worse with system size. Various kinds of heuristic methods are therefore often used to find reasonably good solutions. The artificial neural network (ANN) approach falls within this category. In contrast to most other methods, the ANN approach does not fully or partly explore the discrete statespace. Rather, it "feels" its way in a fuzzy manner through an interpolating, continuous space towards good solutions, and allows for a probabilistic interpretation. Key elements in this approach are the meanfield (MF) approximation (Hopfield and Tank, 1985; Peterson and S¨oderberg, 1989), annealing, and for many problems the Potts formulation (Peterson and S¨oderberg, 1989). Recently, also propagator methods have proven most valuable for handling
Searching in The Plane
 INFORMATION AND COMPUTATION
, 1991
"... In this paper we initiate a new area of study dealing with the best way to search a possibly unbounded region for an object. The model for our search algorithms is that we must pay costs proportional to the distance of the next probe position relative to our current position. This model is meant to ..."
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Cited by 146 (0 self)
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In this paper we initiate a new area of study dealing with the best way to search a possibly unbounded region for an object. The model for our search algorithms is that we must pay costs proportional to the distance of the next probe position relative to our current position. This model is meant to give a realistic cost measure for a robot moving in the plane. We also examine the effect of decreasing the amount of a priori information given to search problems. Problems of this type are very simple analogues of nontrivial problems on searching an unbounded region, processing digitized images, and robot navigation. We show that for some simple search problems, the relative information of knowing the general direction of the goal is much higher than knowing the distance to the goal.
AntNet: A Mobile Agents Approach to Adaptive Routing
, 1997
"... This paper introduces AntNet, a new routing algorithm for communications networks. AntNet is an adaptive, distributed, mobileagentsbased algorithm whichwas inspired by recentwork on the ant colony metaphor. We apply AntNet to a datagram network and compare it with both static and adaptive stateof ..."
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Cited by 134 (6 self)
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This paper introduces AntNet, a new routing algorithm for communications networks. AntNet is an adaptive, distributed, mobileagentsbased algorithm whichwas inspired by recentwork on the ant colony metaphor. We apply AntNet to a datagram network and compare it with both static and adaptive stateoftheart routing algorithms. We ran experiments for various paradigmatic temporal and spatial traffic distributions. AntNet showed both very good performance and robustness under all the experimental conditions with respect to its competitors.
A Comparative Study of Language Support for Generic Programming
, 2003
"... Many modern programming languages support basic generic programming, sufficient to implement typesafe polymorphic containers. Some languages have moved beyond this basic support to a broader, more powerful interpretation of generic programming, and their extensions have proven valuable in practice. ..."
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Cited by 102 (16 self)
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Many modern programming languages support basic generic programming, sufficient to implement typesafe polymorphic containers. Some languages have moved beyond this basic support to a broader, more powerful interpretation of generic programming, and their extensions have proven valuable in practice. This paper reports on a comprehensive comparison of generics in six programming languages: C , Standard ML, Haskell, Eiffel, Java (with its proposed generics extension), and Generic C#. By implementing a substantial example in each of these languages, we identify eight language features that support this broader view of generic programming. We find these features are necessary to avoid awkward designs, poor maintainability, unnecessary runtime checks, and painfully verbose code. As languages increasingly support generics, it is important that language designers understand the features necessary to provide powerful generics and that their absence causes serious difficulties for programmers.
The image foresting transform: Theory, algorithms, and applications
 IEEE TPAMI
, 2004
"... The image foresting transform (IFT) is a graphbased approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definiti ..."
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Cited by 95 (31 self)
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The image foresting transform (IFT) is a graphbased approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it—a generalization of Dijkstra’s algorithm—with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.
SEMIRING FRAMEWORKS AND ALGORITHMS FOR SHORTESTDISTANCE PROBLEMS
, 2002
"... We define general algebraic frameworks for shortestdistance problems based on the structure of semirings. We give a generic algorithm for finding singlesource shortest distances in a weighted directed graph when the weights satisfy the conditions of our general semiring framework. The same algorit ..."
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Cited by 90 (20 self)
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We define general algebraic frameworks for shortestdistance problems based on the structure of semirings. We give a generic algorithm for finding singlesource shortest distances in a weighted directed graph when the weights satisfy the conditions of our general semiring framework. The same algorithm can be used to solve efficiently classical shortest paths problems or to find the kshortest distances in a directed graph. It can be used to solve singlesource shortestdistance problems in weighted directed acyclic graphs over any semiring. We examine several semirings and describe some specific instances of our generic algorithms to illustrate their use and compare them with existing methods and algorithms. The proof of the soundness of all algorithms is given in detail, including their pseudocode and a full analysis of their running time complexity.
Hierarchical encoded path views for path query processing: An optimal model and its performance evaluation
 IEEE Transactions on Knowledge and Data Engineering
, 1998
"... Abstract—Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precompu ..."
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Cited by 87 (2 self)
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Abstract—Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) diskbased solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in outofdate query results). We propose a hierarchical encoded path view (HEPV) model that addresses all three problems. By hierarchically encoding partial paths, HEPV reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over HEPV are optimal. We present complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an indepth experimental evaluation of HEPV based on both synthetic and real GIS networks. Our results confirm that HEPV offers advantages over alternative path finding approaches in terms of performance and space efficiency. Index Terms—Path queries, path view materialization, hierarchical path search, GIS databases, graph partitioning. 1
Approximate Solutions to Markov Decision Processes
, 1999
"... One of the basic problems of machine learning is deciding how to act in an uncertain world. For example, if I want my robot to bring me a cup of coffee, it must be able to compute the correct sequence of electrical impulses to send to its motors to navigate from the coffee pot to my office. In fact, ..."
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Cited by 82 (10 self)
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One of the basic problems of machine learning is deciding how to act in an uncertain world. For example, if I want my robot to bring me a cup of coffee, it must be able to compute the correct sequence of electrical impulses to send to its motors to navigate from the coffee pot to my office. In fact, since the results of its actions are not completely predictable, it is not enough just to compute the correct sequence; instead the robot must sense and correct for deviations from its intended path. In order for any machine learner to act reasonably in an uncertain environment, it must solve problems like the above one quickly and reliably. Unfortunately, the world is often so complicated that it is difficult or impossible to find the optimal sequence of actions to achieve a given goal. So, in order to scale our learners up to realworld problems, we usually must settle for approximate solutions. One representation for a learner's environment and goals is a Markov decision process or MDP. ...
A Distributed Reinforcement Learning Scheme for Network Routing
 In Proceedings of the 1993 International Workshop on Applications of Neural Networks to Telecommunications
, 1993
"... In this paper we describe a selfadjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times. In ..."
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Cited by 77 (2 self)
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In this paper we describe a selfadjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times. In simple experiments involving a 36node, irregularly connected network, this learning approach proves superior to a nonadaptive algorithm based on precomputed shortest paths. The authors would like to thank for their support the Bellcore Cognitive Science Research Group, the National Defense Science and Engineering Graduate fellowship program, and National Science Foundation Grant IRI9214873. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of Bellcore, the National Science Foundation or the U.S. Government. Keywords: Reinforcement learning, Network routing 1 Int...
Shortest path algorithms: An evaluation using real road networks
 Transportation Science
, 1998
"... The classic problem of finding the shortest path over a network has been the target of many research efforts over the years. These research efforts have resulted in a number of different algorithms and a considerable amount of empirical findings with respect to performance. Unfortunately, prior rese ..."
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Cited by 77 (1 self)
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The classic problem of finding the shortest path over a network has been the target of many research efforts over the years. These research efforts have resulted in a number of different algorithms and a considerable amount of empirical findings with respect to performance. Unfortunately, prior research does not provide a clear direction for choosing an algorithm when one faces the problem of computing shortest paths on real road networks. Most of the computational testing on shortest path algorithms has been based on randomly generated networks, which may not have the characteristics of real road networks. In this paper, we provide an objective evaluation of 15 shortest path algorithms using a variety of real road networks. Based on the evaluation, a set of recommended algorithms for computing shortest paths on real road networks is identified. This evaluation should be particularly useful to researchers and practitioners in operations research, management science, transportation, and Geographic Information Systems. The computation of shortest paths is an important task in many network and transportation related analyses. The development, computational testing, and efficient implementation of shortest path algorithms have remained important research topics within related disciplines such as operations