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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:
A genetic algorithm for the maximum 2-packing set problem
Autorzy:
Trejo-Sánchez, Joel Antonio
Fajardo-Delgado, Daniel
Gutierrez-Garcia, J. Octavio
Powiązania:
https://bibliotekanauki.pl/articles/330154.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maximum 2-packing set
genetic algorithms
graph algorithms
algorytm genetyczny
algorytm grafowy
Opis:
Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair of vertices in S is at least 3 and S has the maximum cardinality. In this paper, we present a genetic algorithm for the maximum 2-packing set problem on arbitrary graphs, which is an NP-hard problem. To the best of our knowledge, this work is a pioneering effort to tackle this problem for arbitrary graphs. For comparison, we extended and outperformed a well-known genetic algorithm originally designed for the maximum independent set problem. We also compared our genetic algorithm with a polynomial-time one for the maximum 2-packing set problem on cactus graphs. Empirical results show that our genetic algorithm is capable of finding 2-packing sets with a cardinality relatively close (or equal) to that of the maximum 2-packing sets. Moreover, the cardinality of the 2-packing sets found by our genetic algorithm increases linearly with the number of vertices and with a larger population and a larger number of generations. Furthermore, we provide a theoretical proof demonstrating that our genetic algorithm increases the fitness for each candidate solution when certain conditions are met.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 173-184
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Stochastic Models of Progression of Cancer and Their Use in Controlling Cancer-Related Mortality
Autorzy:
Kimmel, M.
Gorlova, O. Y.
Powiązania:
https://bibliotekanauki.pl/articles/908159.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
medycyna
statystyka
lung cancer
genetic susceptibility
environmental exposure
tumor growth
statistical modeling
simulation
Opis:
A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under different scenarios. Genetic indicators of susceptibility to lung cancer should be used to define the highest-risk subgroups of the high-risk behavior population (smokers). The calibration and validation of the model requires applying our techniques to a variety of data sets available, including public registry data of the SEER type, data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 279-287
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Identification of local elastic parameters in heterogeneous materials using a parallelized femu method
Autorzy:
Petureau, L.
Doumalin, P.
Bremand, F.
Powiązania:
https://bibliotekanauki.pl/articles/265841.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
elastyczność
algorytm genetyczny
obliczenia równoległe
identification
elasticity
heterogeneous materials
genetic algorithm
parallel computation
Opis:
In this work, we explore the possibilities of the widespread Finite Element Model Updating method (FEMU) in order to identify the local elastic mechanical properties in heterogeneous materials. The objective function is defined as a quadratic error of the discrepancy between measured fields and simulated ones. We compare two different formulations of the function, one based on the displacement fields and one based on the strain fields. We use a genetic algorithm in order to minimize these functions. We prove that the strain functional associated with the genetic algorithm is the best combination. We then improve the implementation of the method by parallelizing the algorithm in order to reduce the computation cost. We validate the approach with simulated cases in 2D.
Źródło:
International Journal of Applied Mechanics and Engineering; 2019, 24, 4; 140-156
1734-4492
2353-9003
Pojawia się w:
International Journal of Applied Mechanics and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vehicle-routing optimization for municipal solid waste collection using genetic algorithm: the case of southern Nablus city
Optymalizacja zbiórki odpadów komunalnych z wykorzystaniem algorytmu genetycznego: studium przypadku – miasto Nablus Palestyna
Autorzy:
Assaf, R.
Saleh, Y.
Powiązania:
https://bibliotekanauki.pl/articles/396484.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
solid waste collection
Vehicle Routing Problem
genetic algorithm
integer program
zbieranie odpadów
algorytm genetyczny
Opis:
Municipalities are responsible for solid waste collection for environmental, social and economic purposes. Practices of municipalities should be effective and efficient, with the objectives of reducing the total incurred costs in the solid waste collection network concurrently achieving the highest service level. This study aims at finding the best routes of solid waste collection network in Nablus city-Palestine. More specifically, the study seeks the optimal route that minimizes the total travelled distance by the trucks and hence the resulted costs. The current situation is evaluated and the problem is modelled as a Vehicle Routing Problem (VRP). The VRP is then optimized via a genetic algorithm. Specifically, compared to the current situation, the trucks total travelled distance was reduced by 66%, whereas the collection time was reduced from 7 hours per truck-trip to 2.3 hours. The findings of this study is useful for all municipality policy makers that are responsible for solid waste collection.
Gminy są odpowiedzialne za zbiórkę i gromadzenie odpadów stałych z powodów środowiskowych, społecznych i gospodarczych. Działania te powinny być efektywne i skuteczne. Celem jest zmniejszenie poniesionych kosztów na zbiórkę i transport odpadów stałych przy jednoczesnym osiągnięciu najwyższego poziomu usług. W artykule poddano analizie metody zbierania odpadów stałych w mieście Nablus-Palestyna. Wyznaczono optymalną trasę, która minimalizuje całkowitą odległość pokonywaną przez ciężarówki, a tym samym koszty. W praktyce sytuacja jest analizowana na bieżąco i modelowana w VRP (Vehicle routing problem). Następnie VRP jest optymalizowany za pomocą algorytmu genetycznego. W porównaniu z obecną sytuacją, całkowite odległości pojazdów w wyniku analizy zostały zmniejszone o 66%, a czas zbierania odpadów komunalnych został skrócony z 7 do 2,3 godziny. Wyniki tego badania są przydatne dla wszystkich decydentów odpowiedzialnych za gromadzenie odpadów stałych.
Źródło:
Civil and Environmental Engineering Reports; 2017, No. 26(3); 43-57
2080-5187
2450-8594
Pojawia się w:
Civil and Environmental Engineering Reports
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing a ship course controller by applying the adaptive backstepping method
Autorzy:
Witkowska, A.
Śmierzchalski, R.
Powiązania:
https://bibliotekanauki.pl/articles/331255.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
autopilot
sterowanie adaptacyjne
sterowanie nieliniowe
algorytm genetyczny
adaptive control
nonlinear control
backstepping
genetic algorithms
Opis:
The article discusses the problem of designing a proper and efficient adaptive course-keeping control system for a seagoing ship based on the adaptive backstepping method. The proposed controller in the design stage takes into account the dynamic properties of the steering gear and the full nonlinear static maneuvering characteristic. The adjustable parameters of the achieved nonlinear control structure were tuned up by using the genetic algorithm in order to optimize the system performance. A realistic full-scale simulation model of the B-481 type vessel including wave and wind effects was applied to simulate the control algorithm by using time domain analysis.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 985-997
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Advances in parallel heterogeneous genetic algorithms for continuous optimization
Autorzy:
Alba, E.
Luna, F.
Nebro, A. J.
Powiązania:
https://bibliotekanauki.pl/articles/907622.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytm genetyczny
optymalizacja ciągła
konwergencja przedwczesna
parallel genetic algorithms
continuous optimization
premature convergence
heterogeneity
Opis:
In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. We introduce here a further extension of Hy3, called Hy4, that uses 16 islands arranged in a hypercube of four dimensions. Thus, two new faces with different exploration/exploitation search capabilities are added to the search performed by Hy3. We analyze the importance of running a synchronous versus an asynchronous version of the models considered. The results indicate that the proposed Hy4 model overcomes the Hy3 performance because of its improved balance between exploration and exploitation that enhances the search. Finally, we also show that the async Hy4 model scales better than the sync one.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 317-333
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:
A backstepping approach to ship course control
Autorzy:
Witkowska, A.
Tomera, M.
Śmierzchalski, R.
Powiązania:
https://bibliotekanauki.pl/articles/911245.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie nieliniowe
algorytm genetyczny
sterowanie statkiem
funkcja Lapunowa
backstepping
nonlinear control
genetic algorithms
ship control
Lyapunov function
Opis:
As an object of course control, the ship is characterised by a nonlinear function describing static manoeuvring characteristics that reflect the steady-state relation between the rudder deflection and the rate of turn of the hull. One of the methods which can be used for designing a nonlinear ship course controller is the backstepping method. It is used here for designing two configurations of nonlinear controllers, which are then applied to ship course control. The parameters of the obtained nonlinear control structures are tuned to optimise the operation of the control system. The optimisation is performed using genetic algorithms. The quality of operation of the designed control algorithms is checked in simulation tests performed on the mathematical model of a tanker. In order to obtain reference results to be used for comparison with those recorded for nonlinear controllers designed using the backstepping method, a control system with the PD controller is examined as well.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2007, 17, 1; 73-85
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fusion Technology of Neural Networks and Fuzzy Systems: a Chronicled Progression from the Laboratory to Our Daily Lives
Autorzy:
Takagi, H.
Powiązania:
https://bibliotekanauki.pl/articles/911142.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sieć neuronowa
system rozmyty
algorytmy
cooperative models
neural networks
fuzzy systems
genetic algorithms
real world applications
overview
Opis:
We chronicle the research on the fusion technology of neural networks and fuzzy systems (NN+FS), the models that have been proposed from this research, and the commercial products and industrial systems that have adopted these models. First, we review the NN+FS research activity during the early stages of their development in Japan, the US, and Europe. Next, following the classifi- cation of NN+FS models, we show the ease of fusing these technologies based on the similarities of the data flow network structures and the non-linearity realization strategies of NNs and FSs. Then, we describe several models and applications of NN+FS. Finally, we introduce some important and recently developed NN+FS patents.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 647-673
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of compressed heat exchanger efficiency by using genetic algorithm
Autorzy:
Ghorbani, M.
Ranjbar, S. F.
Powiązania:
https://bibliotekanauki.pl/articles/266267.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
wymiennik ciepła
spadek ciśnienia
pojemność cieplna
optymalizacja
algorytm genetyczny
heat exchanger
pressure drop
heat capacity
optimization
genetic algorithm
Opis:
Due to the application of coil-shaped coils in a compressed gas flow exchanger and water pipe flow in airconditioner devices, air conditioning and refrigeration systems, both industrial and domestic, need to be optimized to improve exchange capacity of heat exchangers by reducing the pressure drop. Today, due to the reduction of fossil fuel resources and the importance of optimal use of resources, optimization of thermal, mechanical and electrical devices has gained particular importance. Compressed heat exchangers are the devices used in industries, especially oil and petrochemical ones, as well as in power plants. So, in this paper we try to optimize compressed heat exchangers. Variables of the functions or state-of-the-machine parameters are optimized in compressed heat exchangers to achieve maximum thermal efficiency. To do this, it is necessary to provide equations and functions of the compressed heat exchanger relative to the functional variables and then to formulate the parameter for the gas pressure drop of the gas flow through the blades and the heat exchange surface in relation to the heat duty. The heat transfer rate to the gas-side pressure drop is maximized by solving the binary equation system in the genetic algorithm. The results show that using optimization, the heat capacity and the efficiency of the heat exchanger improved by 15% and the pressure drop along the path significantly decreases.
Źródło:
International Journal of Applied Mechanics and Engineering; 2019, 24, 2; 461-472
1734-4492
2353-9003
Pojawia się w:
International Journal of Applied Mechanics and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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