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Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we use the spectral partitioning to detect communities in a co-authorship network. The partitioning depends heavily on the weighting of the underlying network...
We use genetic programming to evolve accurate predictors (fuzzy rules) that are deployed to estimate the tension in a power plant generator. The meta-heuristic is compared to the finite element method that was used to compute estimated tension. In contrast to the finite element method, the fuzzy predictor (once found) approximates the tension in the facility quickly and with sufficient precision.
This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental...
Fuzzy sets and fuzzy logic can be used for efficient data mining, classification, and value prediction. We propose a genetically evolved fuzzy predictor to estimate the output of a Photovoltaic Power Plant. Photovoltaic Power Plants (PVPPs) are classified as power energy sources with unstable supply of electrical energy. It is necessary to back up power energy from PVPPs for stable electric network...
Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures and mine data from real world networks. In this paper, we use a simple spectral decomposition to analyze a co-authorship network. We use a straightforward approach based on algebraic connectivity and characteristic...
Parallel computing can be very useful for meta-heuristic algorithms, because it can reduce time to find a solution with given properties. On the other hand parallel programming is generally more difficult than the sequential one and we need more development time to get a working parallel solution. We are developing a tool named Kaira which is intended for modelling, debugging, and automated generation...
Data compression algorithms were usually designed for data processing symbol by symbol. The input symbols of these algorithms are usually taken from the ASCII table, i.e. the size of the input alphabet is 256 symbols which are representable by 8-bit numbers. Several other techniques were developed-syllable-based compression, which uses the syllable as a basic compression symbol, and word-based compression,...
The recent time has seen the rise of consumer grade massively parallel environments. Powerful GPUs and multi-core processors became widely available and easy to use programming APIs such as nVidia CUDA, OpenCL, and DirectCompute simplify the development of applications that can utilize them. In this environment, the nature inspired meta-heuristics can be in suitable cases implemented in parallel without...
Data compression algorithms were usually designed for data processing symbol by symbol. Symbols are usually characters or bytes, but several other techniques may be used. The most well-known approach is using syllables or words as symbols. Another approach is to take 2-grams, 3-grams or any n-grams as a symbols. All these approaches has pros and cons, but none of them is the best for any file. This...
In this paper we present a new dynamic nature inspired algorithm for the enhancement of routing in problematic parts of the road network affected by traffic jams, accidents and so on. The algorithm is intended to improve the quality of navigation in the critical sections of road network or in its parts affected by accidents. By the Ant Colony Algorithm we search for new up-to-date information characterizing...
Fuzzy sets and fuzzy logic can be used for efficient data classification by fuzzy rules and fuzzy classifiers. This paper presents an application of genetic programming to the evolution of fuzzy classifiers based on extended Boolean queries. Extended Boolean queries are well known concept in the area of fuzzy information retrieval. An extended Boolean query represents a complex soft search expression...
Genetic algorithms have been successfully used for searching for quasigroups with good properties. In this study we extend previous work done on evolutionary search for quasigroups by defining new fitness functions based on selected algebraical properties of the quasigroups. Introduced fitness functions make use of heterogeneous sequences generated during the exponentiation of quasigroups elements...
Linear Ordering Problem (LOP) is a well know NP-hard problem combinatorial optimization problem attractive for its complexity, rich library of test data and variety of real world applications. In this paper, we use differential evolution accelerated by the GPU using the nVidia CUDA platform to find good LOP solutions. The well known LOLIB library was used to evaluate the efficiency and precision of...
As the volume and variety of information sources, especially on the World Wide Web (WWW), continues to grow, the requirements imposed on search applications are steadily increasing. The amount of available data is growing and so do user demands. Search application should provide the users with accurate, sensible responses to their requests. It is difficult to provide information that accurately matches...
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