A multi-objective neural network evolution method and multi-objective neural network evolution apparatus are provided. In the method, multiple neural network groups are trained under different environments, respectively. At least one neural network is selected from multiple neural networks of each neural network group, and multiple genes of at least two of the selected neural networks are randomly exchanged to generate multiple mixed genomes and use the same to construct multiple mixed neural networks. The constructed mixed neural networks are evolved by using a multi-objective genetic algorithm, and a fitness value of each of the evolved mixed neural networks is calculated. A single multi-objective neural network adapted for the multiple environments is determined according to the fitness values of the evolved mixed neural networks. |