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Wyszukujesz frazę "genetic programming" wg kryterium: Temat

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    Wyświetlanie 1-4 z 4
    Tytuł:
    GPFIS - control : a genetic fuzzy system for control tasks
    Autorzy:
    Koshiyama, A. S.
    Vellasco, M. M. B. R.
    Tanscheit, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/91648.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    genetic fuzzy controler
    GFC
    genetic programming fuzzy inference system for control
    GPFISControl
    multigene genetic programming
    inverted pendulum
    Opis:
    This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFISControl). It is based on MultiGene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFISControl are considered: the CartCentering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFISControl in relation to other GFCs found in the literature.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 167-179
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An analysis of the performance of genetic programming for realised volatility forecasting
    Autorzy:
    Yin, Z.
    O’Sullivan, C.
    Brabazon, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91765.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    realised volatility
    genetic programming
    high frequency data
    Opis:
    Traditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 155-172
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing island model genetic programming by controlling frequent trees
    Autorzy:
    Ono, Keiko
    Hanada, Yoshiko
    Kumano, Masahito
    Kimura, Masahiro
    Powiązania:
    https://bibliotekanauki.pl/articles/91860.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    genetic programming
    island model
    frequent tree-based migration strategy
    Opis:
    In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit frequent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 51-65
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Administration-and communication-aware IP core mapping in scalable multiprocessor system-on-chips via evolutionary computing
    Autorzy:
    Guderian, F.
    Schaffer, R.
    Fettweis, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91539.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    intellectual property
    IP
    IP core
    mapping
    system-on-chips
    mixed-integer linear programming
    MILP
    genetic algorithm
    GA
    administration
    communication
    Opis:
    In this paper, an efficient mapping of intellectual property (IP) cores onto a scalable multiprocessor system-on-chip with a k-ary 2-mesh network-on-chip is performed. The approach is to place more affine IP cores closer to each other reducing the number of traversed routers. Affinity describes the pairwise relationship between the IP cores quantified by an amount of exchanged communication or administration data. A genetic algorithm (GA) and a mixed-integer linear programming (MILP) solution use the affinity values in order to optimize the IP core mappings. The GA generates results faster and with a satisfactory quality relative to MILP. Realistic benchmark results demonstrate that a tradeoff between administration and communication affinity significantly improves application performance.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 133-146
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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