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    Wyświetlanie 1-4 z 4
    Tytuł:
    Image reconstruction with the use of evolutionary algorithms and cellular automata
    Autorzy:
    Seredyński, F.
    Skaruz, J.
    Piraszewski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/106216.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    image reconstruction
    evolution algorithms
    cellular automata
    genetic algorithm
    Opis:
    In the paper we present a new approach to the image reconstruction problem based on evolution algorithms and cellular automata. Two-dimensional, nine state cellular automata with the Moore neighbourhood perform reconstruction of an image presenting a human face. Large space of automata rules is searched through efficiently by the genetic algorithm (GA), which finds a good quality rule. The experimental results show that the obtained rule allows to reconstruct an image with even 70% damaged pixels. Moreover, we show that the rule found in the genetic evolution process can be applied to the reconstruction of images of the same class but not presented during the evolutionary one.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 4; 39-49
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Game Theoretical Model Applied to Scheduling in Grid Computing
    Autorzy:
    Świtalski, P.
    Seredyński, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/93040.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    scheduling
    game theory
    prisoner's dilemma
    genetic algorithm
    grid
    task
    job
    Opis:
    We consider a grid computational model which consist of a number of computation nodes and a number of users. Each user generates a computation load (jobs) requesting computational and communication resources. A deadline for each job is also defined. We propose a scheduling algorithm which is based on Iterated Prisoner's Dilemma (IPD) under the Random Pairing game, where nodes (players) of the grid system decide about their behavior: cooperate or defect. In this game players play a game with randomly chosen players and receive payoffs. Each player has strategies which define its decision. Genetic algorithm (GA) is used to evolve strategies to optimize a criterion related to scheduling problem. In this paper we show that GA is able to discover a strategy in the IPD model providing a cooperation between node-players, which permits to solve scheduling problem in grid.
    Źródło:
    Studia Informatica : systems and information technology; 2007, 2(9); 19-27
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Function optimization using metaheuristics
    Autorzy:
    Pilski, M.
    Seredyński, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/92887.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    particle swarm optimization (PSO)
    artificial immune system
    genetic algorithm
    function optimization
    Opis:
    The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.
    Źródło:
    Studia Informatica : systems and information technology; 2006, 1(7); 77-91
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Decentralized job scheduling in the cloud based on a spatially generalized Prisoner’s Dilemma game
    Autorzy:
    Gąsior, J.
    Seredyński, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/329736.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    job scheduling
    multiobjective optimization
    genetic algorithm
    prisoner's dilemma
    cellular automata
    harmonogramowanie zadań
    optymalizacja wielokryterialna
    algorytm genetyczny
    dylemat więźnia
    automat komórkowy
    Opis:
    We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 737-751
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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