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1
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A hybrid approach for scheduling transportation networks

100%
Dridi M., Kacem I.
International Journal of Applied Mathematics and Computer Science
|
2004
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tom 14
|
nr 3
397-409
EN
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.
2
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Evolutionary learning of rich neural networks in the Bayesian model selection framework

100%
Matteucci M., Spadoni D.
International Journal of Applied Mathematics and Computer Science
|
2004
|
tom 14
|
nr 3
423-440
EN
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.
3
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A factor graph based genetic algorithm

88%
Hoda Helmi B., Rahmani A., Pelikan M.
International Journal of Applied Mathematics and Computer Science
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2014
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tom 24
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nr 3
621-633
EN
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.
4
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The island model as a Markov dynamic system

88%
Schaefer R., Byrski A., Smołka M.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 4
971-984
EN
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.
5
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A backstepping approach to ship course control

88%
Witkowska A., Tomera M., Smierzchalski R.
International Journal of Applied Mathematics and Computer Science
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2007
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tom 17
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nr 1
73-85
EN
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.
6
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Approximation of phenol concentration using novel hybrid computational intelligence methods

75%
Pławiak P., Tadeusiewicz R.
International Journal of Applied Mathematics and Computer Science
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2014
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tom 24
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nr 1
165-181
EN
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.
7
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Genetic and combinatorial algorithms for optimal sizing and placement of active power filters

75%
Maciążek M., Grabowski D., Pasko M.
International Journal of Applied Mathematics and Computer Science
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2015
|
tom 25
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nr 2
269-279
EN
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.
8
Content available remote

Niching mechanisms in evolutionary computations

63%
Kowalczuk Z., Białaszewski T.
International Journal of Applied Mathematics and Computer Science
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2006
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tom 16
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nr 1
59-84
EN
Different types of niching can be used in genetic algorithms (GAs) or evolutionary computations (ECs) to sustain the diversity of the sought optimal solutions and to increase the effectiveness of evolutionary multi-objective optimization solvers. In this paper four schemes of niching are proposed, which are also considered in two versions with respect to the method of invoking: a continuous realization and a periodic one. The characteristics of these mechanisms are discussed, while as their performance and effectiveness are analyzed by considering exemplary multi-objective optimization tasks both of a synthetic and an engineering (FDI) design nature.
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