Bat-BP: a new bat based back-propagation algorithm for efficient data classification
Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigate...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6549/ http://eprints.uthm.edu.my/6549/1/212.pdf |
| Summary: | Training neural networks particularly back propagation algorithm is a complex task of
great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat
algorithm is becoming a popular method in solving many complex optimization problems. Thus,
this study investigates the use of Bat algorithm along with back-propagation neural network
(BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to
enhance the convergence rate. This study intends to show the superiority (time performance and
quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard
neural network training algorithms. The performance of the proposed Bat-BP algorithm is then
compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using
Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine
learning repository are used to train the network. The simulation results show that the efficiency of
BPNN training process is highly enhanced when combined with BAT algorithm |
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