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Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks

Raminder Kaur, Bikrampal Kaur Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
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  1. Raminder Kaur and Bikrampal Kaur. Article: Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks. International Journal of Applied Information Systems 8(5):16-19, March 2015. BibTeX

    	author = "Raminder Kaur and Bikrampal Kaur",
    	title = "Article: Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 5,
    	pages = "16-19",
    	month = "March",
    	note = "Published by Foundation of Computer Science, New York, USA"


Artificial Neural Network (ANN) is a powerful artificial tool suitable for solving combinatorial problems such as prediction and classification. The performance of ANN is highly dependent upon its architecture and connection weights. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and efficient learning algorithm has a significant role to enhance its performance. The process of weight training is a complex continuous optimization problem. This paper deals with the application of swarm intelligence based algorithm, Bacterial Foraging Optimization (BFO) for training feed-forward and cascade-forward ANNs. BFO algorithm which is based on the foraging strategy of bacteria is adopted to train the connection weights and to evolve the ANN learning and accuracy. The experiments performed on dataset taken from promise repository verify the potential of BFO algorithm and showed that classification accuracy of BFO-ANN is more than the traditional ANN.


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Artificial Neural Networks, Bacterial Foraging Optimization Algorithm