The Ph.D. thesis presents a newly designed novel Genetic Algorithm namely, CAscaded Genetic Algorithm (CAGA). The ultimate goal of the method is to provide the enhancement of the conventional GAs. CAGA eliminates the possibility to be trapped in a local optimum which is the primary drawback of the conventional GA. The hybrid version of CAGA has very well demonstrates this criterion. The cascaded method has been applied on data clustering, function optimization and shape recognition. The results of CAGA shows that it outperforms or equally performs compared to the relevant genetic algorithms.
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The Ph.D. thesis presents a newly designed novel Genetic Algorithm namely, CAscaded Genetic Algorithm (CAGA). The ultimate goal of the method is to provide the enhancement of the conventional GAs. CAGA eliminates the possibility to be trapped in a local optimum which is the primary drawback of the conventional GA. The hybrid version of CAGA has very well demonstrates this criterion. The cascaded method has been applied on data clustering, function optimization and shape recognition. The results of CAGA shows that it outperforms or equally performs compared to the relevant genetic algorithms.
Gautam Garai is a Scientist and got undergraduate, post-graduate and Ph.D. degrees in Computer Sc. & Engg. from Jadavpur University, Kolkata, India. He has published several research papers. His research interests are in Evolutionary Algos., Data Clustering and Classification, Pattern Recognition, Data Mining and Bio-informatics.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. Different Applications of Cascaded Genetic Algorithms | Data Clustering, Function Optimization and Shape Recognition | Gautam Garai | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639282702 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 107421802
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