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Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently...
Many natural networks including social networks are scale-free, i.e. the distribution of node degrees in the network is heavy-tailed and follows the power law. In this work we analyze the scale-free properties of selected subnets of a complex co-authorship network induced by significant nodes (authors) and the evolution of these properties in time. The subnets induced by significant authors are sampled...
Industrial plants use many different sensors for processes monitoring and controlling. These sensors generate huge amount of data. These data should be used for improving of the quality of semi and final products in each factory. In this paper, we describe processing of two different datasets acquired from a steel-mill factory using three different methods SVM, Fuzzy Rules and Bayesian classification...
In the last few years, the Graphic Processing Units (GPUs) emerged as an exciting new hardware environment available for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data (SIMD) devices that can nowadays reach...
One of the most important things during a project planning is the estimation of the project cost. Many decisions during project estimation and planning are based on previous experience and competency of a manager. Evaluated completed projects provide rich knowledge about similar decisions and reality. This paper focuses on the comparison of two supportive methods for the approach how to estimate value...
This study proposes a novel design and implementation of Differential Evolution (DE) using the Partitioned Global Address Space (PGAS) parallel computing model and the Unified Parallel C (UPC) programming language. The mapping of DE concepts to UPC features is presented and a DE useful for both many-core shared memory systems and clusters of computers with distributed memory is implemented and evaluated...
Methods based on fuzzy sets and fuzzy logic have proved to be efficient data classifiers and value estimators. This study presents an application of evolutionary evolved fuzzy rules based on the concept of extended Boolean queries to a multi-class data mining problem. Fuzzy rules are used as symbolic classifiers machine-learned from the data and used to label data samples and predict the value of...
Distributed environments and emerging highly-parallel platforms provide a suitable hardware infrastructure for parallel Evolutionary Computation. Partitioned Global Address Space model is a well-known parallel computing model used to implement scalable algorithms for many-core systems and clusters. This study investigates the Unified Parallel C programming language as a tool for implementation of...
Traffic accidents represent a major problem threatening peoples lives, health, and property. Traffic behavior and driving in particular is a social and cultural phenomenon that exhibits significant differences across countries and regions. Therefore, traffic models developed in one country might not be suitable for other countries. Similarly, attributes of importance, dependencies, and patterns found...
Differential evolution is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is suitable for a parallel implementation on multicore systems and on Graphic Processing Units (GPUs) that nowadays reach peak performance of hundreds and thousands...
Artificial immune systems (AIS) represent a family of bio-inspired populational meta-heuristic algorithms successful in solving complex problems. Combinatorial optimization problems constitute a class of problems with a discrete set of solutions. In this study we provide an initial evaluation of the practical results of AIS for two well known combinatorial optimization problems — the linear ordering...
Searching of similar pictures was in the past based mainly on searching of similar picture names. We try to find an effective method how to search pictures by searching of similar information in the picture (histograms, shapes, blocks,). There already are some methods but still not effective enough. In this paper we describe a method where we combine vector quantization (VQ) and fuzzy S-trees. Work...
Renewable energy sources are becoming a significant part of todays energy mix. The unstable production of many renewable energy sources including photovoltaic and wind power plants puts increased demands on power transmission systems and on the power grid as a whole. Soft computing methods can contribute to the prediction of electric energy production of renewable resources and therefore to the reliability...
Data compression is very important today and it will be even more important in the future. Textual data use only limited alphabet - total number of used symbols (letters, numbers, diacritics, dots, spaces, etc.). In most languages, letters are joined into syllables and words. All three approaches are useful in text compression, but none of them is the best for any file. This paper describes a variant...
This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules. Expert identification is an important task in social network analysis and there are several methods to identify people who have experience in given area. In this paper, we propose a hybrid approach where the formal concept analysis is used for finding author's...
Document classification is a well-known problem that is focused on assigning predefined labels or categories to the documents found in the searched collection. Many classical algorithms were developed for solving of this problem. They usually have large time complexity and with increasing number of documents it is necessary to find algorithm which are able to find solution in reasonable time. Such...
Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes...
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules...
This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labeling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management...
General compression algorithms were designed for usage characters as basic symbols. Later, algorithm which used words were developed. The problem is that there is no clear line which defines if is better to use characters or words. In this paper, we developed optimization algorithm based on simulated annealing that selects only several words from all possible words and combine them with character...
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