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supplied. Here it serves the same purpose of the neural learning algorithm. For
instance, we can replace the well- known back-propagation algorithm by a GA
based scheme. Secondly, GA can be employed to determine the structure of a
neural net. Thus when the number of neurons in one or more hidden layer
cannot be guessed properly, we may employ GA to solve this problem.
Thirdly, GA may be employed to automatically adjust the parameters of a
prototype learning equation. This has many useful applications in adaptive
control, where the adaptation of the control law is realized with GA.
15.6.1 GA as an Alternative to
Back-propagation Learning
The back-propagation learning adjusts the weights of a feed-forward neural
net by employing the principles of steepest descent learning. One main
drawback of this classical algorithm is trapping at local minima. Due to
mutation in a GA, it has the characteristics of hill climbing, and thus can
overcome the difficulty of trapping at local minima. The principle of using GA
for neural learning is presented below.
X1
X2
X3
W 6
W1
Fig. 15. 10: Illustrating use of GA in neural learning.
Here, we considered a three layered neural net with weights (fig.
15.10). Let the input pattern and the output patterns be [ X1 X2 X3] T and [ Y1
Y2] T respectively. Let the weights of the first layer and the second layer
G1
G4
Y1
Y2
Fig.