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    Wyświetlanie 1-14 z 14
    Tytuł:
    Towards a linguistic description of dependencies in data
    Autorzy:
    Batyrshin, I.
    Wagenknecht, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908041.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    informatyka
    fuzzy approximation
    linguistic term
    fuzzy rule
    genetic algorithm
    Opis:
    The problem of a linguistic description of dependencies in data by a set of rules Rk: "If X is Tk then Y is Sk" is considered, where Tk's are linguistic terms like SMALL, BETWEEN 5 AND 7 describing some fuzzy intervals Ak. Sk's are linguistic terms like DECREASING and QUICKLY INCREASING describing the slopes pk of linear functions yk=pkx +qk approximating data on Ak. The decision of this problem is obtained as a result of a fuzzy partition of the domain X on fuzzy intervals Ak, approximation of given data {xi,yi}, i=1,...,n by linear functions yk=pkx+qk on these intervals and by re-translation of the obtained results into linguistic form. The properties of the genetic algorithm used for construction of the optimal partition and several methods of data re-translation are described. The methods are illustrated by examples, and potential applications of the proposed methods are discussed.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 391-401
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Two meta-heuristic algorithms for scheduling on unrelated machines with the late work criterion
    Autorzy:
    Wang, Wen
    Chen, Xin
    Musial, Jędrzej
    Blazewicz, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/330022.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    late work minimization
    unrelated machines
    tabu search
    genetic algorithm
    minimalizacja opóźnienia
    przeszukiwanie tabu
    algorytm genetyczny
    Opis:
    A scheduling problem in considered on unrelated machines with the goal of total late work minimization, in which the late work of a job means the late units executed after its due date. Due to the NP-hardness of the problem, we propose two meta-heuristic algorithms to solve it, namely, a tabu search (TS) and a genetic algorithm (GA), both of which are equipped with the techniques of initialization, iteration, as well as termination. The performances of the designed algorithms are verified through computational experiments, where we show that the GA can produce better solutions but with a higher time consumption. Moreover, we also analyze the influence of problem parameters on the performances of these metaheuristics.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 573-584
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach
    Autorzy:
    Yan, F.
    Dridi, M.
    El Moudni, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/329874.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    autonomous vehicle
    autonomous intersection management
    genetic algorithm
    dynamic programming
    heuristics
    pojazd autonomiczny
    algorytm genetyczny
    programowanie dynamiczne
    Opis:
    This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 183-200
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid approach for scheduling transportation networks
    Autorzy:
    Dridi, M.
    Kacem, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/907640.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system transportowy
    regulacja ruchu
    algorytm genetyczny
    optymalizacja wielokryterialna
    transportation systems
    traffic regulation
    genetic algorithm
    multicriteria optimization
    Opis:
    In this paper, we consider a regulation problem of an urban transportation network. From a given timetable, we aim to find a new schedule of multiple vehicles after the detection of a disturbance at a given time. The main objective is to find a solution maximizing the level of service for all passengers. This problem was intensively studied with evolutionary approaches and multi-agent techniques, but without identifying its type before. In this paper, we formulate the problem as a classical one in the case of an unlimited vehicle capacity. In the case of a limited capacity and an integrity constraint, the problem becomes difficult to solve. Then, a new coding and well-adapted operators are proposed for such a problem and integrated in a new evolutionary approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 397-409
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary learning of rich neural networks in the Bayesian model selection framework
    Autorzy:
    Matteucci, M.
    Spadoni, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/907642.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    model Bayesa
    algorytm genetyczny
    Rich Neural Networks
    Bayesian model selection
    genetic algorithm
    Bayesian fitness
    Opis:
    In this paper we focus on the problem of using a genetic algorithm for model selection within a Bayesian framework. We propose to reduce the model selection problem to a search problem solved using evolutionary computation to explore a posterior distribution over the model space. As a case study, we introduce ELeaRNT (Evolutionary Learning of Rich Neural Network Topologies), a genetic algorithm which evolves a particular class of models, namely, Rich Neural Networks (RNN), in order to find an optimal domain-specific non-linear function approximator with a good generalization capability. In order to evolve this kind of neural networks, ELeaRNT uses a Bayesian fitness function. The experimental results prove that ELeaRNT using a Bayesian fitness function finds, in a completely automated way, networks well-matched to the analysed problem, with acceptable complexity.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 423-440
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    FSPL: A meta-learning approach for a filter and embedded feature selection pipeline
    Autorzy:
    Lazebnik, Teddy
    Rosenfeld, Avi
    Powiązania:
    https://bibliotekanauki.pl/articles/2201020.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    feature selection pipeline
    meta learning
    no free lunch
    autoML
    genetic algorithm
    wybór funkcji
    metauczenie
    algorytm genetyczny
    Opis:
    There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on n = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model’s accuracy compared to the second-best meta-learning method.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 103--115
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Genetic and combinatorial algorithms for optimal sizing and placement of active power filters
    Autorzy:
    Maciążek, M.
    Grabowski, D.
    Pasko, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330809.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    power quality
    optimization
    active power filter
    harmonics
    genetic algorithm
    combinatorial algorithm
    jakość energii
    energetyczny filtr aktywny
    algorytm genetyczny
    algorytm kombinatoryczny
    Opis:
    The paper deals with cost effective compensator placement and sizing. It becomes one of the most important problems in contemporary electrical networks, in which voltage and current waveform distortions increase year-by-year reaching or even exceeding limit values. The suppression of distortions could be carried out by means of three types of compensators, i.e., passive filters, active power filters and hybrid filters. So far, passive filters have been more popular mainly because of economic reasons, but active and hybrid filters have some advantages which should cause their wider application in the near future. Active power filter placement and sizing could be regarded as an optimization problem. A few objective functions have been proposed for this problem. In this paper we compare solutions obtained by means of combinatorial and genetic approaches. The theoretical discussion is followed by examples of active power filter placement and sizing.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 2; 269-279
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A factor graph based genetic algorithm
    Autorzy:
    Helmi, B. H.
    Rahmani, A. T.
    Pelikan, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330811.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    optimization problem
    genetic algorithm
    estimation
    distribution algorithm
    factor graph
    matrix factorization
    problem optymalizacji
    algorytm genetyczny
    algorytm estymacji rozkładu
    faktoryzacja macierzy
    Opis:
    We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 621-633
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A genetic algorithm based optimized convolutional neural network for face recognition
    Autorzy:
    Karlupia, Namrata
    Mahajan, Palak
    Abrol, Pawanesh
    Lehana, Parveen K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2201023.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    convolutional neural network
    genetic algorithm
    deep learning
    evolutionary technique
    sieć neuronowa konwolucyjna
    algorytm genetyczny
    uczenie głębokie
    technika ewolucyjna
    Opis:
    Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A robust algorithm to solve the signal setting problem considering different traffic assignment approaches
    Autorzy:
    Adacher, L.
    Gemma, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/330229.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    genetic algorithm
    surrogate method
    traffic signal synchronization
    traffic assignment
    simulation model
    algorytm genetyczny
    metoda zastępcza
    synchronizacja sygnału ruchu
    model symulacji
    Opis:
    In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization (PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 815-826
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    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
    Tytuł:
    Nonlinear system identification with a real-coded genetic algorithm (RCGA)
    Autorzy:
    Cherif, I.
    Fnaiech, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/329753.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    blind nonlinear identification
    Volterra series
    higher order cumulants
    real-coded genetic algorithm
    szereg Volterry
    kumulanta wyższego rzędu
    algorytm genetyczny kodowania rzeczywistego
    Opis:
    This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 863-875
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Approximation of phenol concentration using novel hybrid computational intelligence methods
    Autorzy:
    Pławiak, P.
    Tadeusiewicz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/907935.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    soft computing
    neural network
    genetic algorithm
    fuzzy system
    evolutionary neural system
    pattern recognition
    chemometrics
    przetwarzanie miękkie
    sieć neuronowa
    algorytm genetyczny
    system rozmyty
    rozpoznawanie obrazu
    chemometria
    Opis:
    This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 165-181
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic parametric fault detection in complex analog systems based on a method of minimum node selection
    Autorzy:
    Bilski, A.
    Wojciechowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/330761.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    complex analog system
    support vector machine (SVM)
    tabu search
    genetic algorithm
    parametric fault detection
    system analogowy
    maszyna wektorów wspierających
    metoda tabu search
    algorytm genetyczny
    detekcja uszkodzeń
    Opis:
    The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 3; 655-668
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-14 z 14

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