1,254 research outputs found

    Learning-Based Approaches for Graph Problems: A Survey

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    Over the years, many graph problems specifically those in NP-complete are studied by a wide range of researchers. Some famous examples include graph colouring, travelling salesman problem and subgraph isomorphism. Most of these problems are typically addressed by exact algorithms, approximate algorithms and heuristics. There are however some drawback for each of these methods. Recent studies have employed learning-based frameworks such as machine learning techniques in solving these problems, given that they are useful in discovering new patterns in structured data that can be represented using graphs. This research direction has successfully attracted a considerable amount of attention. In this survey, we provide a systematic review mainly on classic graph problems in which learning-based approaches have been proposed in addressing the problems. We discuss the overview of each framework, and provide analyses based on the design and performance of the framework. Some potential research questions are also suggested. Ultimately, this survey gives a clearer insight and can be used as a stepping stone to the research community in studying problems in this field.Comment: v1: 41 pages; v2: 40 page

    A novel learning automata game with local feedback for parallel optimization of hydropower production

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    Master's thesis Information- and communication technology IKT590 - University of Agder 2017Hydropower optimization for multi-reservoir systems is classi ed as a combinatorial optimization problem with large state-space that is particularly di cult to solve. There exist no golden standard when solving such problems, and many proposed algorithms are domain speci c. The literature describes several di erent techniques where linear programming approaches are extensively discussed, but tends to succumb to the curse of dimensionality problem when the state vector dimensions increase. This thesis introduces LA LCS, a novel learning automata algorithm that utilizes a parallel form of local feedback. This enables each individual automaton to receive direct feedback, resulting in faster convergence. In addition, the algorithm is implemented using a parallel architecture on a CUDA enabled GPU, along with exhaustive and random search. LA LCS has been veri ed through several scenarios. Experiments show that the algorithm is able to quickly adapt and nd optimal production strategies for problems of variable complexity. The algorithm is empirically veri ed and shown to hold great promise for solving optimization problems, including hydropower production strategies

    Computing procedures for a learning machine

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    Cellular Automata Applications in Shortest Path Problem

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    Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

    Distributed learning automata-based scheme for classification using novel pursuit scheme

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    Author's accepted manuscript.Available from 03/03/2021.This is a post-peer-review, pre-copyedit version of an article published in Applied Intelligence. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10489-019-01627-w.acceptedVersio

    The 1st Conference of PhD Students in Computer Science

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