The invention discloses a neural
network construction system and method based on a variable-length
genetic algorithm, wherein the construction
system includes a sequentially connected
training set generation module, a network initialization module, a network training module, a network update module, a network optimization module, Network selection module, iteration number update module and
network generation module. The general idea of the construction method of this scheme is to generate variable-length chromosomes in the initialization step, and randomly add BN components to specific genes of each
chromosome. Through the training on the
training set, the neural
network structure representation corresponding to each
chromosome is obtained through training, and the
offspring chromosomes are selected. Subsequently, in the
crossover step, the length of the crossed chromosomes is not fixed, and a growth and shrinking strategy is used to generate
daughter chromosomes. Then use the traditional
mutation and environment selection operations to complete the selection of
offspring chromosomes, repeat the above, and decode the obtained best chromosomes into the corresponding
neural network architecture.