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Comparative analysis of genetic algorithm, simulated annealing and cutting angle method for artificial neural networks

conference contribution
posted on 2005-12-01, 00:00 authored by R Ghosh, M Ghosh, John YearwoodJohn Yearwood, A Bagirov
Neural network learning is the main essence of ANN. There are many problems associated with the multiple local minima in neural networks. Global optimization methods are capable of finding global optimal solution. In this paper we investigate and present a comparative study for the effects of probabilistic and deterministic global search method for artificial neural network using fully connected feed forward multi-layered perceptron architecture. We investigate two probabilistic global search method namely Genetic algorithm and Simulated annealing method and a deterministic cutting angle method to find weights in neural network. Experiments were carried out on UCI benchmark dataset. © Springer-Verlag Berlin Heidelberg 2005.

History

Volume

3587 LNAI

Pagination

62-70

Location

Leipzig, Germany

Start date

2005-07-09

End date

2005-07-11

ISSN

0302-9743

eISSN

1611-3349

ISBN-10

3540269231

Publication classification

EN.1 Other conference paper

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publisher

Springer

Place of publication

Berlin, Germany

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