Genetic Algorithm Based On Back Propagation Network
Neural networks (NNs) are the adaptive system that changes its structure based on external or internal information that flows through the network. Neural network solve problems by self-learning and self-organizing.
Back Propagation Network (BPN) is a method of training multi-layer neural networks. Here learning occurs during this training phase.
The steps involved are:
− The pattern of activation arriving at the output layer is compared with the correct output pattern to calculate an error signal.
− The error signal is then back-propagated from output to input for adjusting the weights in each layer of the BPN.
− The Back-Propagation searches on the error surface using gradient descent method to minimize error E = 1/2 Σ ( T j – O j )2 where Tjis target output and Ojis the calculated output by the network.
Limitations of BPN :
− BPN can recognize patterns similar to those they have learnt, but do not have the ability to recognize new patterns.
− BPN must be sufficiently trained to extract enough general features applicable to both seen and unseen; over training to network may have undesired effects.
Genetic Algorithms (GAs)are adaptive search and optimization algorithms, mimic the principles of nature.
− GAs are different form traditional search and
− Optimization exhibit simplicity, ease of operation, minimal requirements, and global perspective.
Hybridization of BPN and GAs
− The BPN determines its weight based on gradient search technique and therefore it may encounter a local minima problem.
− GAs do not guarantee to find global optimum solution, but are good in finding quickly good acceptable solution.
− Therefore, hybridization of BPN and GAs are expected to provide many advantages compare to what they alone can.