Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Genetic algorithm" wg kryterium: Temat

  Lista filtrów

Dostawca treści

Wyrównaj
Wyświetlanie 1-15 z 375
Tytuł:
A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO
Autorzy:
Nagaraj, B.
Vijayakumar, P.
Powiązania:
https://bibliotekanauki.pl/articles/384767.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
ant colony algorithm
evolutionary program
genetic algorithm particle swarm optimization and soft computing
Opis:
Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 2; 42-48
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
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 generalized varying-domain optimization method for fuzzy goal programming with priorities based on a genetic algorithm
Autorzy:
Li, S. Y.
Hu, C. F.
Teng, C. J.
Powiązania:
https://bibliotekanauki.pl/articles/970454.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
rozmyte programowanie celowe
priorytet
algorytm genetyczny
fuzzy goal programming
priorities
SQP
genetic algorithm
GENOCOP III
Opis:
This paper proposes a generalized domain optimization method for fuzzy goal programming with different priorities. According to the three possible styles of the objective function, the domain optimization method and its generalization are correspondingly proposed. This method can generate the results consistent with the decision-maker's priority expectations, according to which the goal with higher priority may have higher level of satisfaction. However, the reformulated optimization problem may be nonconvex for the reason of the nature of the original problem and the introduction of the varying-domain optimization method. It is possible to obtain a local optimal solution for nonconvex programming by the SQP algorithm. In order to get the global solution of the new programming problem, the co-evolutionary genetic algorithm, called GENOCOP III, is used instead of the SQP method. In this way the decision-maker can get. the optimum of the optimization problem. We demonstrate the power of this proposed method based on genetic algorithm by illustrative examples.
Źródło:
Control and Cybernetics; 2004, 33, 4; 633-652
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A generative approach to hull design for a small watercraft
Autorzy:
Karczewski, Artur
Kozak, Janusz
Powiązania:
https://bibliotekanauki.pl/articles/32917891.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
method
generative design
yacht
optimisation
genetic algorithm
Opis:
In the field of ocean engineering, the task of spatial hull modelling is one of the most complicated problems in ship design. This study presents a procedure applied as a generative approach to the design problems for the hull geometry of small vessels using elements of concurrent design with multi-criteria optimisation processes. Based upon widely available commercial software, an algorithm for the mathematical formulation of the boundary conditions, the data flow during processing and formulae for the optimisation processes are developed. As an example of the application of this novel approach, the results for the hull design of a sailing yacht are presented.
Źródło:
Polish Maritime Research; 2023, 1; 4-12
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy
Autorzy:
Sadiqi, Assia
El Abbassi, Ikram
El Barkany, Abdellah
Darcherif, Moumen
El Biyaali, Ahmed
Powiązania:
https://bibliotekanauki.pl/articles/1841396.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
scheduling
maintenance
genetic algorithm
branch
bound
MILP
modeling
optimization
CPLEX
Python
Opis:
The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.
Źródło:
Management and Production Engineering Review; 2020, 11, 4; 138-148
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
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 genetic algorithm for vehicle routing in logistic networks with practical constraints
Autorzy:
Koloch, Grzegorz
Lewandowski, Michał
Zientara, Marcin
Grodecki, Grzegorz
Matuszak, Piotr
Kantorski, Igor
Nowackig, Adam
Powiązania:
https://bibliotekanauki.pl/articles/1981356.pdf
Data publikacji:
2021-12-30
Wydawca:
Główny Urząd Statystyczny
Tematy:
rich vehicle routing problem
brownfield
hubs and satellites
genetic algorithm
Opis:
We optimise a postal delivery problem with time and capacity constraints imposed on vehicles and nodes of the logistic network. Time constraints relate to the duration of routes, whereas capacity constraints concern technical characteristics of vehicles and postal operation outlets. We consider a method which can be applied to a brownfield scenario, in which capacities of outlets can be relaxed and prospective hubs identified. As a solution, we apply a genetic algorithm and test its properties both in small case studies and in a simulated problem instance of a larger (i.e. comparable with real-world instances) size. We show that the genetic operators we employ are capable of switching between solutions based on direct origin-to-destination routes and solutions based on transfer connections, depending on what is more beneficial in a given problem instance. Moreover, the algorithm correctly identifies cases in which volumes should be shipped directly, and those in which it is optimal to use transfer connections within a single problem instance, if an instance in question requires such a selection for optimality. The algorithm is thus suitable for determining hubs and satellite locations. All considerations presented in this paper are motivated by real-life problem instances experienced by the Polish Post, the largest postal service provider in Poland, in its daily plans of delivering postal packages, letters and pallets.
Źródło:
Przegląd Statystyczny; 2021, 68, 3; 16-40
0033-2372
Pojawia się w:
Przegląd Statystyczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems
Autorzy:
Chung, Chia-Shin
Flynn, James
Rom, Walter
Staliński, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/475000.pdf
Data publikacji:
2012
Wydawca:
Fundacja Upowszechniająca Wiedzę i Naukę Cognitione
Tematy:
genetic algorithm
scheduling
permutation flowshop
tardiness
Opis:
The m-machine, n-job, permutation flowshop problem with the total tardiness objective is a common scheduling problem, known to be NP-hard. Branch and bound, the usual approach to finding an optimal solution, experiences difficulty when n exceeds 20. Here, we develop a genetic algorithm, GA, which can handle problems with larger n. We also undertake a numerical study comparing GA with an optimal branch and bound algorithm, and various heuristic algorithms including the well known NEH algorithm and a local search heuristic LH. Extensive computational experiments indicate that LH is an effective heuristic and GA can produce noticeable improvements over LH.
Źródło:
Journal of Entrepreneurship, Management and Innovation; 2012, 8, 2; 26-43
2299-7075
2299-7326
Pojawia się w:
Journal of Entrepreneurship, Management and Innovation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm-based approach for flexible job shop rescheduling problem with machine failure interference
Autorzy:
Liang, Zhongyuan
Zhong, Peisi
Zhang, Chao
Yang, Wenlei
Xiong, Wei
Yang, Shihao
Meng, Jing
Powiązania:
https://bibliotekanauki.pl/articles/27320976.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
genetic algorithm
rescheduling
machine failure
flexible job shop scheduling
Opis:
Rescheduling is the guarantee to maintain the reliable operation of production system process. In production system, the original scheduling scheme cannot be carried out when machine breaks down. It is necessary to transfer the production tasks in the failure cycle and replan the production path to ensure that the production tasks are completed on time and maintain the stability of production system. To address this issue, in this paper, we studied the event-driven rescheduling policy in dynamic environment, and established the usage rules of right-shift rescheduling and complete rescheduling based on the type of interference events. And then, we proposed the rescheduling decision method based on genetic algorithm for solving flexible job shop scheduling problem with machine fault interference. In addition, we extended the "mk" series of instances by introducing the machine fault interference information. The solution data show that the complete rescheduling method can respond effectively to the rescheduling of flexible job shop scheduling problem with machine failure interference.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 171784
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid ant colony for multiresponse mixed-integer problems
Autorzy:
Kushwaha, S.
Mukherjee, I.
Powiązania:
https://bibliotekanauki.pl/articles/409419.pdf
Data publikacji:
2012
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
ant colony optimization(ACO)
desirability functions
genetic algorithm (GA)
multiple response optimization(MRO)
Opis:
In this paper, a hybrid ant colony optimization (ACO) is used to solve a multiple response optimization problem with mixed-integer (MI) search space. The work reported in this paper may be clasified into three part. The first part discusses on relevant litratures and the methodology to solve multiple response optimization problem. The second part provide details on the working principal, parameter tuning of a hybrid ACO proposed for mixed integer state space. In the hybrid ACO, genetic algorithm (GA) is used for intensification of the search strategy. Standard single response (objective) test functions are selected to verify the suitability of hybrid ACO. The third part of this research work illustrates the application of the hybrid ACO in a multiple response optimization (MRO) problem. Statistical experimentation, partial least square regression, 'maximin' desirability function, and hybrid ACO is used to solve the MRO problem. The results confirm the suitability of the hybid ACO for a typical MI MRO problem.
Źródło:
Research in Logistics & Production; 2012, 2, 4; 317-327
2083-4942
2083-4950
Pojawia się w:
Research in Logistics & Production
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:
A logistic optimization for the vehicle routing problem through a case study in the food industry
Autorzy:
Akpinar, Muhammet Enes
Powiązania:
https://bibliotekanauki.pl/articles/1835487.pdf
Data publikacji:
2021
Wydawca:
Wyższa Szkoła Logistyki
Tematy:
vehicle routing problem
time windows
optimization
metaheuristic algorithm
genetic algorithm
trasa pojazdu
okna czasowe
optymalizacja
algorytm metaheurystyczny
algorytm genetyczny
Opis:
In this study, the food delivery problem faced by a food company is discussed. There are seven different regions where the company serves food and a certain number of customers in each region. The time of requesting food for each customer varies according to the shift situation. This type of problem is referred to as a vehicle routing problem with time windows in the literature and the main aim of the study is to minimize the total travel distance of the vehicles. The second aim is to determine which vehicle will follow which route in the region by using the least amount of vehicle according to the desired mealtime. Methods: In this study, genetic algorithm methodology is used for the solution of the problem. Metaheuristic algorithms are used for problems that contain multiple combinations and cannot be solved in a reasonable time. Thus in this study, a solution to this problem in a reasonable time is obtained by using the genetic algorithm method. The advantage of this method is to find the most appropriate solution by trying possible solutions with a certain number of populations. Results: Different population sizes are considered in the study. 1000 iterations are made for each population. According to the genetic algorithm results, the best result is obtained in the lowest population size. The total distance has been shortened by about 14% with this method. Besides, the number of vehicles in each region and which vehicle will serve to whom has also been determined. This study, which is a real-life application, has provided serious profitability to the food company even from this region alone. Besides, there have been improvements at different rates in each of the seven regions. Customers' ability to receive service at any time has maximized customer satisfaction and increased the ability to work in the long term. Conclusions: The method and results used in the study were positive for the food company. However, the metaheuristic algorithm used in this study does not guarantee an optimal result. Therefore, mathematical models or simulation models can be considered in terms of future studies. Besides, in addition to the time windows problem, the pickup problem can also be taken into account and different solution proposals can be developed.
Źródło:
LogForum; 2021, 17, 3; 387-397
1734-459X
Pojawia się w:
LogForum
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Method of Identifying Dynamic Parameters of Generating Units Based on Dynamic Response During Disturbances
Metoda identyfikacji parametrów dynamicznych jednostek wytwórczych na podstawie przebiegów pozakłóceniowych
Autorzy:
Bajor, M.
Kosmecki, M.
Wilk, M.
Powiązania:
https://bibliotekanauki.pl/articles/396830.pdf
Data publikacji:
2013
Wydawca:
ENERGA
Tematy:
genetic algorithm
dynamic equivalent
dynamic parameters identification
algorytm genetyczny
ekwiwalentowanie
identyfikacja paremetrów dynamicznych
Opis:
Simulation of power system operation requires proper dynamic representation of power system components in the simulation software, i.e. correct structures and parameters of the models. However, in large, modern power systems, a lack of required information is often encountered. Identification is one of the ways to obtain the missing data. In this paper an identification method based on an implementation of genetic algorithm is proposed. Its prerequisite is the availability of the unit's response to the disturbance, for example obtained from transient fault recorders. The method is tested for a single unit, for which the response was obtained beforehand using the model with known parameters. The method can also be used to facilitate the creation of dynamic equivalents of larger parts of the power system, which is also presented in the paper.
W artykule zaproponowano wykorzystanie algorytmu genetycznego do identyfikacji parametrów dynamicznych jednostek wytwórczych lub ekwiwalentów zastępujących obszary systemu elektroenergetycznego z wykorzystaniem przebiegów pozakłóceniowych. Zamieszczono również wyniki dwóch rodzajów testów oprogramowania, stworzonego na podstawie opracowanej metody. Testy zostały wykonane poprzez dobór parametrów pojedynczej jednostki wytwórczej oraz dla jednostki zastępczej, mającej za zadanie odwzorowanie zachowania dynamicznego większego obszaru systemu.
Źródło:
Acta Energetica; 2013, 4; 4-13
2300-3022
Pojawia się w:
Acta Energetica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new empirical correlation for estimating bubble point pressure using the genetic algorithm
Autorzy:
Heidarian, M.
Karimnezhad, M.
Schaffie, M.
Ranjbar, M.
Powiązania:
https://bibliotekanauki.pl/articles/184188.pdf
Data publikacji:
2017
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
bubble point pressure
empirical correlations
Middle East
genetic algorithm
PVT properties
Opis:
In this paper, a new and more accurate correlation to predict bubble point pressure (Pb) for Middle East crudes by using the genetic algorithm (GA) is attempted. For this purpose, a total of 286 data sets of different crude oils from Middle East reservoirs were used as training data for constructing the correlation. The general form of the correlation was found by several regressive examinations. To improve the correlation, the genetic algorithm was applied. To validate the correlation, 143 data sets of different crudes from Middle East reservoirs which were different from the training data were used as test data for calculating mean absolute relative error (MARE) and correlation coefficient (R2) between the predicted values from the proposed correlation and the experimental values. In addition, the MARE and R2 were calculated for previous correlation in the test data. The results show that the proposed correlation is more accurate than all of the previous correlations exclusively for Middle East crudes.
Źródło:
Geology, Geophysics and Environment; 2017, 43, 1; 33-41
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 375

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies