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Wyszukujesz frazę "Clustering, Genetic Algorithm" wg kryterium: Temat

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    Wyświetlanie 1-4 z 4
    Tytuł:
    Genetic algorithm for clustering, finding the number of clusters
    Autorzy:
    Śmigielski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/1373528.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
    Tematy:
    genetic algorithm
    clustering
    variable length chromosome
    cube
    Opis:
    In this paper a genetic algorithm for clustering is proposed. The algorithm is based on the variable length chromosomes and the notion of local points density in the clustered set. Its role is to identify the number of clusters in the clustered set and to partition this set into particular clusters. The tests were conducted for two different sets of two dimensional data. The algorithm performed well in both cases. The tests presented the ability of the algorithm to partition the subsets combined with the thin dense area into separate clusters.
    Źródło:
    Schedae Informaticae; 2011, 20; 101-113
    0860-0295
    2083-8476
    Pojawia się w:
    Schedae Informaticae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recognizing Sets in Evolutionary Multiobjective Optimization
    Autorzy:
    Gajda-Zagórska, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/308467.pdf
    Data publikacji:
    2012
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    basin of attraction
    clustering
    genetic algorithm
    multiobjective optimization
    Opis:
    Among Evolutionary Multiobjective Optimization Algorithms (EMOA) there are many which find only Paretooptimal solutions. These may not be enough in case of multimodal problems and non-connected Pareto fronts, where more information about the shape of the landscape is required. We propose a Multiobjective Clustered Evolutionary Strategy (MCES) which combines a hierarchic genetic algorithm consisting of multiple populations with EMOA rank selection. In the next stage, the genetic sample is clustered to recognize regions with high density of individuals. These regions are occupied by solutions from the neighborhood of the Pareto set. We discuss genetic algorithms with heuristic and the concept of well-tuning which allows for theoretical verification of the presented strategy. Numerical results begin with one example of clustering in a single-objective benchmark problem. Afterwards, we give an illustration of the EMOA rank selection in a simple two-criteria minimization problem and provide results of the simulation of MCES for multimodal, multi-connected example. The strategy copes with multimodal problems without losing local solutions and gives better insight into the shape of the evolutionary landscape. What is more, the stability of solutions in MCES may be analyzed analytically.
    Źródło:
    Journal of Telecommunications and Information Technology; 2012, 1; 74-82
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving Population-Based Algorithms with Fitness Deterioration
    Autorzy:
    Wolny, A.
    Schaefer, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/308437.pdf
    Data publikacji:
    2011
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    basin of attraction
    clustering
    fitness deterioration
    genetic algorithm
    optics
    sequential niching
    Opis:
    This work presents a new hybrid approach for supporting sequential niching strategies called Cluster Supported Fitness Deterioration (CSFD). Sequential niching is one of the most promising evolutionary strategies for analyzing multimodal global optimization problems in the continuous domains embedded in the vector metric spaces. In each iteration CSFD performs the clustering of the random sample by OPTICS algorithm and then deteriorates the fitness on the area occupied by clusters. The selection pressure pushes away the next-step sample (population) from the basins of attraction of minimizers already recognized, speeding up finding the new ones. The main advantages of CSFD are low memory an computational complexity even in case of large dimensional problems and high accuracy of deterioration obtained by the flexible cluster definition delivered by OPTICS. The paper contains the broad discussion of niching strategies, detailed definition of CSFD and the series of the simple comparative tests.
    Źródło:
    Journal of Telecommunications and Information Technology; 2011, 4; 31-44
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm
    Autorzy:
    El-Samak, A. F.
    Ashour, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/91810.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    combinatorial optimization problem
    travel salesman problem
    genetic algorithm
    evolutionary computation algorithm
    affinity propagation clustering technique
    AP
    problem optymalizacji kombinatorycznej
    algorytm genetyczny
    obliczenia ewolucyjne
    Opis:
    Combinatorial optimization problems, such as travel salesman problem, are usually NPhard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 239-245
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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