The combination of genetic algorithm and neural network for
weight training consists of three major phases. The first phase is
to decide the representation of connection weights, i.e., whether
we use a binary strings form or directly use a real number form
to represent the connection weights. Since this paper uses a real
code genetic algorithm, what we have to do is just to set each neuron’s
connection weights and bias to its correspondent gene segments.
However, it is difficult to reach convergence using the
binary-encoded simple genetic algorithms (SGA) to solve the optimization
problems that have too much design variables. So, a real
code genetic algorithm was used to overcome the disadvantages of
SGA. The second step is the evaluation on the fitness of these connection
weights by constructing the corresponding neural network.
The objective function (shown in Eq. (3)) is selected as the
fitness function directly. Because of the generalization of ANN, its
model can be used as the knowledge source for the optimization
algorithm. This method can compute the objective function in real
time. The evaluation criterion of the individuals was ‘‘LOWS-BEST”.
The third one is applying the evolutionary process such as selection,
crossover, and mutation operations by a genetic algorithm
according to its fitness. The evolution stops when the fitness is
smaller than a predefined value.