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    Wyświetlanie 1-3 z 3
    Tytuł:
    Agent-based optimization of advisory strategy parameters
    Autorzy:
    Polnik, M.
    Kumięga, M.
    Byrski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/307586.pdf
    Data publikacji:
    2013
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    global optimization
    memetic computing
    mult-agent computing
    Opis:
    In this paper, an application of Evolutionary Multiagent Systems (EMAS) and its memetic version to the optimization of advisory strategy (in this case, Sudoku advisory strategy) is considered. The problem is tackled using an EMAS, which has already proven as a versatile optimization technique. Results obtained using EMAS and Parallel Evolutionary Algorithm (PEA) are compared. After giving an insight to the possibilities of decision support in Sudoku solving, an exemplary strategy is defined. Then EMAS and its memetic versions are discussed, and experimental results concerning comparison of EMAS and PEA presented.
    Źródło:
    Journal of Telecommunications and Information Technology; 2013, 2; 49-55
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The island model as a Markov dynamic system
    Autorzy:
    Schaefer, R.
    Byrski, A.
    Smołka, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/331253.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    algorytm genetyczny
    analiza asymptotyczna
    optymalizacja globalna
    algorytm ewolucyjny równoległy
    łańcuch Markova
    genetic algorithms
    asymptotic analysis
    global optimization
    parallel evolutionary algorithms
    Markov chain modeling
    Opis:
    Parallel multi-deme genetic algorithms are especially advantageous because they allow reducing the time of computations and can perform a much broader search than single-population ones. However, their formal analysis does not seem to have been studied exhaustively enough. In this paper we propose a mathematical framework describing a wide class of island-like strategies as a stationary Markov chain. Our approach uses extensively the modeling principles introduced by Vose, Rudolph and their collaborators. An original and crucial feature of the framework we propose is the mechanism of inter-deme agent operation synchronization. It is important from both a practical and a theoretical point of view. We show that under a mild assumption the resulting Markov chain is ergodic and the sequence of the related sampling measures converges to some invariant measure. The asymptotic guarantee of success is also obtained as a simple issue of ergodicity. Moreover, if the cardinality of each island population grows to infinity, then the sequence of the limit invariant measures contains a weakly convergent subsequence. The formal description of the island model obtained for the case of solving a single-objective problem can also be extended to the multi-objective case.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 971-984
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Asymptotic guarantee of success for multi-agent memetic systems
    Autorzy:
    Byrski, A.
    Schaefer, R.
    Smołka, M.
    Cotta, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/201942.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    computational multi-agent systems
    asymptotic analysis
    global optimization
    parallel evolutionary algorithms
    Markov chain modeling
    Opis:
    The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent’s actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms. The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed. The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 1; 257-278
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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