The evolutionary heuristics applied to the analysis of multidimensional data

• Autorzy: Jasińska-Suwada A.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

This paper describes the pattern recognition system for analysis of multidimensional data, based on natural meta-heuristics. The system consists of tree modules: preprocessing, feature extraction and clustering. Feature extraction module is based on Molecular Dynamic (MD). In clustering are used two natural methods: Simulated Annealing (SA) and Taboo Search (TS). The system is used to analyze an evolving population of individuals equipped with 'genetic codes'. Clustering module extracts groups of data with similar genetic code named clusters and make of possible to observe their geographical localization. The feature extraction verifies the clustering and allows analyzing of clustering patterns, their shapes and the distances between them.

Słowa kluczowe

EN

multidimensional data; pattern recognition; metaheuristics; molecular dynamics; MD; simulated annealing; SA; Taboo Search; TS

PL

dane wielowymiarowe; rozpoznawanie wzorców; metaheurystyka; dynamika molekularna; wyżarzanie symulowane; przeszukiwanie tabu

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 163--170
• Opis fizyczny: Bibliogr. 13 poz., tab., rys.

Twórcy

autor

Jasińska-Suwada A.

• University of Technology, Institute of Telecomputing, Cracow, Poland,

Bibliografia

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