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1

Heterogeneous fog generated with the effect of light scattering and blur

Gawron M., Boryczka U.

Journal of Applied Computer Science

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2018

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Vol. 26, nr 2

31--43

EN

The development of computer graphics forces new requirements on the developers, which will make the virtual world more similar to the real world. One of these elements is the simulation of fog. Common fog algorithms mix the color of the scene with the color of the fog over a certain distance. However, one feature of the naturally foggy scenery is ignored. With the distance and density of the fog, the observed scenery or individual objects become more blurred. In this paper we will present our implementation of the distance fog in the Unreal Engine 4, including the effect of blurring the foggy areas, simulating of light scattering and variations in fog density using noise.

2

Analiza zbioru wiadomości E-mail z zastosowaniem sieci społecznych

Probierz B., Kozak J., Boryczka U.

Zeszyty Naukowe. Studia Informatica / Uniwersytet Szczeciński

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2015

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Nr 36

153--164

PL

W artykule zaproponowane zostało podejście związane z analizą sieci społecznych, a także praktyczne możliwości zastosowania tych sieci w badaniu organizacji pod kątem procesów przepływu wiadomości mailowych pracowników. Celem pracy jest analiza kontaktów pomiędzy poszczególnymi pracownikami korporacji zastosowana do wyznaczenia pracowników - liderów z punktu widzenia rozprzestrzeniania się informacji lub wpływania na osoby będące w bezpośrednim sąsiedztwie. Analiza ta w dalszych pracach powinna przyczynić się do stworzenia algorytmu, którego zastosowanie posłuży do poprawienia dokładności klasyfikacji wiadomości mailowych do poszczególnych folderów w skrzynkach pocztowych pracowników. Zaproponowana metoda została przetestowana na ogólnodostępnym zbiorze danych Enron E-mail.

EN

In this article is proposed an approach based on the Social Network Analysis and its practical applicability in the study of the organization in terms of flow processes e-mails employees. The aim of this paper is to analyze the interaction between individual employees corporation used to designate staff-leaders from the point of view of spreading information or to influence those in the immediate vicinity. This analysis further work should contribute to the creation of the algorithm, the application of which will be used to improve the accuracy of the classification of e-mail messages to specific folders in the mailboxes of employees. The proposed method has been tested on a public dataset Enron Email.

3

Solving the sudoku with the differential evolution

Boryczka U., Juszczuk P.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

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2012

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Z. 9

5-16

EN

In this paper, we present the application of the Differential Evolution (DE) algorithm to solving the combinatorial problem. The advantage of the DE algorithm is its capability of avoiding so-called "local minima" within the considered search space. Thanks to the special operator of the adaptive mutation, it is possible to direct the searching process within the solution space. The DE algorithm applies the selection operator that selects from the child population only the offspring with the greater value of the fitness function in comparison to their parents. An algorithm applied to a combinatorial optimization problem: Sudoku puzzle is presented. Sudoku consists of a nine by nine grid, divided into nine three by three boxes. Each of the eighty-one squares should be filled in with a number between one and nine. In this article we show, that the mutation schema has significant impact on the quality of created solution.

PL

W artykule przedstawimy propozycję zastosowania algorytmu ewolucji różnicowej do rozwiązywania problemów kombinatorycznych. Przewagą ewolucji różnicowej jest zdolność do unikania optimów lokalnych w przestrzeni przeszukiwań. Specjalny operator mutacji pozwala ukierunkować proces poszukiwań rozwiązania. W ewolucji różnicowej stosowany jest operator selekcji, który promuje tylko najlepiej przystosowane osobniki z populacji rodziców i potomków. Przedstawimy zastosowanie opisanego algorytmu do problemu rozwiązywania Sudoku. Sudoku składa się z planszy 9 na 9, podzielonej na 9 sekcji -każda o rozmiarze 3 na 3 elementy. Każda z 81 kratek powinna zostać wypełniona wartością z przedziału 1 do 9. W artykule pokażemy, że ewolucja różnicowa pozwala na rozwiązywanie Sudoku.

4

Ant colony metaphor in a new clustering algorithm

Boryczka U.

Control and Cybernetics

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2010

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Vol. 39, no 2

343-358

EN

Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as k-means algorithm. This proposed approach mimics the clustering behavior observed in real ant colonies. As a case study, this paper focuses on the behavior of clustering procedures in this new approach. The proposed algorithm is evaluated on a number of well-known benchmark data sets. Empirical results clearly show that the ant clustering algorithm (ACA) performs well when compared to other techniques.

5

A Comparative Study of Various Strategies in Differential Evolution

Boryczka U., Juszczuk P., Kłosowicz L.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2009

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z. 169

19-26

EN

This paper presents a comparison of various strategies of differential evolution. Differential evolution (DE) is a simple and powerful optimization method, which is mainly applied to numerical optimization and many other problems (for example: neural network train, filter design or image analysis). The comparison of various modifications (named strategies) of DE algorithm allows to choose the algorithm version which is best adjusted to desirable requirements. Three parameters are tested: speed, accuracy and completeness. The first section of this article presents general optimization problem and says a little about methods used to function optimization. The next section describes differential evolution - basic algorithm is presented. Two different crossover methods, process of initial population creation and basic mutation schema are described. The third section describes the most popular DE strategies. In the fourth section a new modification (called λ-modification) of DE algorithm is presented. Next section provides basic information about four test functions and differential evolution parameters used in research. The paper presents then summary and final conclusions.

6

Initialization with repetitions method for sequential niche techniques

Boryczka U., Kłosowicz L.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2008

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z. 165

51-58

EN

This paper presents a new method of initialization population for sequential niching techniques, in which, evolutionary algorithm (EA) for determining a single local extreme has been employed. Knowledge of the localization of optima, determined in earlier runs of EA has been exploited in this approach. Initialization of a single individual consists in repeating its location until it is placed in the search subspace does not connected with any niche determined earlier. This approach contributes to the enhancement of convergence and to the improvement of achieved results.

7

Comparative study : AGO and EC for TSP

Boryczka U., Skinderowicz R., Swistowski D.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2006

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z. 156

69-76

EN

Evolutionary Computing (EC) and Ant Colony Optimization (ACO) apply stochastic searching, parallel investigation as well as autocatalitic process (or stigmergy) to solve optimization problems. This paper concentrates on the Traveling Salesman Problem (TSP) solved by evolutionary and ACO algorithms. We consider the sets of parameters and operators which influence the acting of these algorithms. Two algorithmic structures emphasizing the selection problem are discussed. We describe experiments performed for different instances of TSP problems. The comparison concludes that evolution, which is exploited especially in evolutionary algorithms, can also be observed in the performance of the ACO approach.

8

Comparison of genetic and ant algorithms

Boryczka U.

Archiwum Informatyki Teoretycznej i Stosowanej

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2000

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T. 12, z. 4

249-264

EN

Genetic and ant algorithms apply stochastic searching, parallel investigation as well as autocatalitic process (or stigmergy) to solve optimization problems. This paper concentrates on the Traveling Salesman Problem (TSP) solved by genetic and ant algorithms. We consider the sets of parameters and operators which influence the acting of these algorithms. Two algorithmic structures emphasizing the selection problem are discussed. We describe the TSP experiments performed for 50 cities. The aim of the comparison is the conclusion that the evolution, which is exploited in genetic algorithms, can improve the performance of ant algorithms.

PL

Artykuł porównuje możliwości algorytmów genetycznych i mrówkowych na przykładzie problemu komiwojażera. Rozważono szereg parametrów mających wpływ na funkcjonowanie wymienionych typów algorytmów. Problem komiwojażera był rozważany dla sieci 50 miast. Celem porównania jest pokazanie, że ewolucja, która jest podstawą funkcjonowania algorytmów genetycznych, zastosowana do algorytmów mrówkowych może zwiększyć ich wydajność.