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Wyszukujesz frazę "evolutionary" wg kryterium: Temat


Tytuł:
Evolutionary algorithms for job-shop scheduling
Autorzy:
Mesghouni, K.
Hammadi, S.
Borne, P.
Powiązania:
https://bibliotekanauki.pl/articles/907245.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
harmonogramowanie produkcji
algorytm ewolucyjny
reprezentacja równoległa
job-shop scheduling
evolutionary algorithms
parallel representation
Opis:
This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 1; 91-103
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
EGIPSYS: An enhanced gene expression programming approach for symbolic refression problems
Autorzy:
Lopes, H. S.
Weinert, W. R.
Powiązania:
https://bibliotekanauki.pl/articles/907638.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
obliczanie ewolucyjne
regresja symboliczna
modelowanie matematyczne
evolutionary computation
symbolic regression
mathematical modelling
systems identification
Opis:
This paper reports a system based on the recently proposed evolutionary paradigm of gene expression programming (GEP). This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed improvements were tested separately, and proved to be advantageous over the basic GEP. EGIPSYS was also applied to four difficult identification problems and its performance was compared with a traditional implementation of genetic programming (LilGP). Overall, EGIPSYS was able to obtain consistently better results than the system using genetic programming, finding less complex solutions with less computational effort. The success obtained suggests the adaptation and extension of the system to other classes of problems.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 375-384
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Advances in model-based fault diagnosis with evolutionary algorithms and neural networks
Autorzy:
Witczak, M.
Powiązania:
https://bibliotekanauki.pl/articles/908460.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
diagnostyka uszkodzeń
algorytmy ewolucyjne
sieci neuronowe
odporność
fault diagnosis
evolutionary algorithms
neural networks
robustness
Opis:
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms and neural networks to fault diagnosis. In particular, a brief introduction to these computational intelligence paradigms is presented, and then a review of their fault detection and isolation applications is performed. Close attention is paid to techniques that integrate the classical and soft computing methods. A selected group of them is carefully described in the paper. The performance of the presented approaches is illustrated with the use of the DAMADICS fault detection benchmark that deals with a valve actuator.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 85-99
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Phenotypic evolution with a mutation based on symmetric alpha-stable distributions
Autorzy:
Obuchowicz, A.
Prętki, P.
Powiązania:
https://bibliotekanauki.pl/articles/907644.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytm ewolucyjny
dystrybucja stabilna
optymalizacja globalna
evolutionary algorithms
Levy-stable distributions
global optimization
surrounding effect
Opis:
Multidimensional Symmetric alpha-Stable (S alpha S) mutations are applied to phenotypic evolutionary algorithms. Such mutations are characterized by non-spherical symmetry for alpha<2 and the fact that the most probable distance of mutated points is not in a close neighborhood of the origin, but at a certain distance from it. It is the so-called surrounding effect (Obuchowicz, 2001b; 2003b). For alpha=2, the S alpha S mutation reduces to the Gaussian one, and in the case of alpha=1, the Cauchy mutation is obtained. The exploration and exploitation abilities of evolutionary algorithms, using S alpha S mutations for different alpha, are analyzed by a set of simulation experiments. The obtained results prove the important influence of the surrounding effect of symmetric alpha-stable mutations on both the abilities considered.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 289-316
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary algorithms and fuzzy sets for discovering temporal rules
Autorzy:
Matthews, S. G.
Gongora, M. A.
Hopgood, A. A.
Powiązania:
https://bibliotekanauki.pl/articles/330148.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fuzzy association rules
temporal association rules
multi objective evolutionary algorithm
reguła asocjacji rozmytej
wieloobiektowy algorytm ewolucyjny
Opis:
A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 855-868
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Customized crossover in evolutionary sets of safe ship trajectories
Autorzy:
Szłapczyński, R.
Szłapczyńska, J.
Powiązania:
https://bibliotekanauki.pl/articles/331257.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytm ewolucyjny
unikanie kolizji statków
system wspomagania decyzji
evolutionary algorithms
ship collision avoidance
decision support systems
Opis:
The paper presents selected aspects of evolutionary sets of safe ship trajectories-a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned within one minute, which enforces speeding up the optimisation process. During the development of the method the authors tested various problem-dedicated crossover operators to obtain the best performance. The results of that research are given here. The paper includes a detailed description of these operators as well as statistical simulation results and examples of experiment results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 999-1009
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolution-fuzzy rule based system with parameterized consequences
Autorzy:
Czekalski, P.
Powiązania:
https://bibliotekanauki.pl/articles/908394.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
strategia ewolucyjna
system rozmyty
system hybrydowy
evolutionary strategy
fuzzy inference system
off-line learning
hybrid system
Opis:
While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps: obtaining an initial set of rules with parameterized consequences using the Michigan approach combined with an evolutionary strategy and a covering algorithm for the training data set; reducing the obtained rule base using a simple genetic algorithm; multi-phase tuning of the fuzzy inference system with parameterized consequences using the Pittsburgh approach and an evolutionary strategy. The paper presents experimental results using popular benchmark data sets regarding system identification and time series prediction, providing a reliable comparison to other learning methods, particularly those based on neuro-fuzzy, clustering and \epsilon-insensitive methods. An examplary fuzzy inference system with parameterized consequences using the Reichenbach implication and the minimum t-norm was implemented to obtain numerical results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 3; 373-385
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A biologically inspired approach to feasible gait learning for a hexapod robot
Autorzy:
Belter, D.
Skrzypczyński, P.
Powiązania:
https://bibliotekanauki.pl/articles/907777.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
identyfikacja modelu
uczenie się ewolucyjne
robot nożny
evolutionary learning
legged robots
gait generation
model identification
reality gap
Opis:
The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 69-84
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:
Codings and operators in two genetic algorithms for the leaf-constrained minimum spanning tree problem
Autorzy:
Julstrom, B. A.
Powiązania:
https://bibliotekanauki.pl/articles/907639.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytm ewolucyjny
algorytm genetyczny
kod Prüfera
evolutionary codings
leaf-constrained spanning trees
Prüfer strings
Blob Code
fixed-length subsets
Opis:
The features of an evolutionary algorithm that most determine its performance are the coding by which its chromosomes represent candidate solutions to its target problem and the operators that act on that coding. Also, when a problem involves constraints, a coding that represents only valid solutions and operators that preserve that validity represent a smaller search space and result in a more effective search. Two genetic algorithms for the leaf-constrained minimum spanning tree problem illustrate these observations. Given a connected, weighted, undirected graph G with n vertices and a bound l, this problem seeks a spanning tree on G with at least l leaves and minimum weight among all such trees. A greedy heuristic for the problem begins with an unconstrained minimum spanning tree on G, then economically turns interior vertices into leaves until their number reaches l. One genetic algorithm encodes candidate trees with Prüfer strings decoded via the Blob Code. The second GA uses strings of length n - l that specify trees' interior vertices. Both GAs apply operators that generate only valid chromosomes. The latter represents and searches a much smaller space. In tests on 65 instances of the problem, both Euclidean and with weights chosen randomly, the Blob-Coded GA cannot compete with the greedy heuristic, but the subset-coded GA consistently identifies leaf-constrained spanning trees of lower weight than the greedy heuristic does, particularly on the random instances.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 385-396
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new approach to nonlinear modelling of dynamic systems based on fuzzy rules
Autorzy:
Bartczuk, Ł.
Przybył, A.
Cpałka, K.
Powiązania:
https://bibliotekanauki.pl/articles/330372.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
nonlinear modelling
dynamic system
fuzzy system
interpretability of fuzzy system
evolutionary algorithm
modelowanie nieliniowe
układ dynamiczny
system rozmyty
algorytm ewolucyjny
Opis:
For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system’s dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 3; 603-621
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A finite-buffer queue with a single vacation policy: An analytical study with evolutionary positioning
Autorzy:
Woźniak, M.
Kempa, W. M.
Gabryel, M.
Nowicki, R. K.
Powiązania:
https://bibliotekanauki.pl/articles/330346.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
finite buffer queue
evolutionary strategy
object positioning
queueing system
busy period
idle time
single vacation
strategia ewolucyjna
pozycjonowanie obiektu
system kolejkowy
czas bezczynności
Opis:
In this paper, application of an evolutionary strategy to positioning a GI/M/1/N-type finite-buffer queueing system with exhaustive service and a single vacation policy is presented. The examined object is modeled by a conditional joint transform of the first busy period, the first idle time and the number of packets completely served during the first busy period. A mathematical model is defined recursively by means of input distributions. In the paper, an analytical study and numerical experiments are presented. A cost optimization problem is solved using an evolutionary strategy for a class of queueing systems described by exponential and Erlang distributions.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 887-900
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
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:
Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems
Autorzy:
Chmaj, G.
Walkowiak, K.
Tarnawski, M.
Kucharzak, M.
Powiązania:
https://bibliotekanauki.pl/articles/330970.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
system obliczeniowy P2P
przetwarzanie rozproszone
optymalizacja
heurystyka
algorytm ewolucyjny
P2P computing system
distributed computing
optimization
heuristics
evolutionary algorithms
Opis:
Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very close to an optimum and can be used for larger problem instances than those solvable by CPLEX or other ILP solvers.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 733-748
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined classifier based on feature space partitioning
Autorzy:
Woźniak, M.
Krawczyk, B.
Powiązania:
https://bibliotekanauki.pl/articles/331294.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozpoznawanie wzorców
system klasyfikujący wielokrotny
algorytm grupowania
algorytm selekcji
algorytm ewolucyjny
pattern recognition
combined classifier
multiple classifier system
clustering algorithm
selection algorithm
evolutionary algorithm
Opis:
This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 855-866
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł

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