Table 2: General framework of GAs for neural network training.

(i) Decode each individual in the current population into a set of connection weights and construct a corresponding ANN with the weights.
(ii) Evaluate the ANN by computing its total mean square error between actual and target outputs.
(iii) Determine fitness of individual as inverse of error. The higher is the error, the lower is the fitness.
(iv) Store the weights for mating pool formation.
(v) Implement search operators such as cross-over/mutation to parents to generate offsprings.
(vi) Calculate fitness for new population.
(vii) Repeat steps (iii) to (vi) until the solution converge.
(viii) Extract optimized weights.