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Wyszukujesz frazę "parameter adaptation" wg kryterium: Temat

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    Wyświetlanie 1-4 z 4
    Tytuł:
    Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
    Autorzy:
    Leon, M.
    Xiong, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/91824.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    differential evolution
    optimization
    parameter adaptation
    Opis:
    Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 2; 103-118
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Correlational parameter tuning by genetic meta-algorithm
    Autorzy:
    Kieś, P.
    Kosiński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/206578.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    adaptacja
    algorytm genetyczny
    optymalizacja
    permutacja kodowa
    strojenie parametrów
    adaptation
    code permutation
    genetic algorithm
    optimization
    parameter tuning
    Opis:
    The general problem of an off-line parameter tuning in the Binary Genetic Algorithm (BGA) is introduced. An example of such a tuning: a class of Correlational Tuning Methods (CTMs) is proposed. The main idea of a CTM is that it uses a mapping called measurement function as an assessment of the BGA's effciency. An example of a measurement function is described and two examples of CTMs: a modified "trials and errors" method and a modified genetic meta-algoritlm (metaBGA) are shown. Finally, experimental results with the metaBGA for four kinds of test fitness functions, where the code permutation is the tuned parameter, are presented.
    Źródło:
    Control and Cybernetics; 2000, 29, 4; 1031-1042
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Self-adaptation of parameters in a learning classifier system ensemble machine
    Autorzy:
    Troć, M.
    Unold, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/907767.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    komputerowe uczenie się
    system klasyfikujący
    sterowanie adaptacyjne
    sterowanie parametryczne
    machine learning
    extended classifier system
    self-adaptation
    adaptive parameter control
    Opis:
    Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 157-174
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative studies on the morphometry and physiology of European populations of the lagoon specialist Cerastoderma glaucum (Bivalvia)
    Autorzy:
    Tarnowska, K.
    Wolowicz, M.
    Chenuil, A.
    Feral, J.-P.
    Powiązania:
    https://bibliotekanauki.pl/articles/48074.pdf
    Data publikacji:
    2009
    Wydawca:
    Polska Akademia Nauk. Instytut Oceanologii PAN
    Tematy:
    ecophysiology
    Baltic Sea
    morphometry
    bivalve
    lagoon
    European population
    Cerastoderma glaucum
    physiological parameter
    seasonal change
    adaptation
    Mediterranean Sea
    cockle
    North Sea
    Bivalvia
    Opis:
    Seasonal changes in the morphometric and physiological parameters of the cockle Cerastoderma glaucum (Bivalvia) from the Baltic Sea (GD), the North Sea (LV), and the Mediterranean Sea (BL) were investigated. The cockles from GD were much smaller than those from other populations due to osmotic stress. The female to male ratios did not differ significantly from 1 : 1. The northern populations (GD, LV) had a monocyclic reproductive pattern, whereas the southern population (BL) seemed to reproduce throughout the year. Seasonal changes in the contents of biochemical components appeared to be correlated with changes in trophic conditions and the reproductive cycle. Protein content was the highest in spring for all the populations. The highest lipid contents and lowest carbohydrate contents were noted in GD and BL in spring, while no marked differences were noted among seasons in LV (probably because the data from both sexes were pooled). Respiration rates in GD were the highest among the populations, which could have been due to osmotic stress. High metabolic rates expressed by high respiration rates in GD and LV in spring and autumn could have resulted from gamete development (in spring) and phytoplankton blooms (in spring and autumn).
    Źródło:
    Oceanologia; 2009, 51, 3; 437-458
    0078-3234
    Pojawia się w:
    Oceanologia
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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