Virtual sample generation (VSG) is an important technology for dealing with small sample learning in some industries. Using evolutionary computation algorithms to solve VSG is a promising way. However, two issues remain unaddressed in the existing VSG approaches: 1) estimating the distribution of original samples and 2) ensuring the authenticity of virtual samples. Thus, this article proposes a novel VSG approach based on the genetic algorithm (GA) combing with information gain and acceptance-rejection sampling (ARS), abbreviated as VSG(3)A(2). First, this work develops the ARS-VAD subalgorithm, by integrating the acceptance rejection sampling method into the crossover and mutation operations of GA. The algorithm ensures that the distribution of offspring attribute values is close to the distribution of original samples at attribute level. Second, this work presents the IG-VSS subalgorithm, which is combined with the idea of minimizing absolute information gain, to find the optimal offspring sample as a virtual sample in each loop, ensuring the authenticity of virtual samples at the sample level. To the best of our knowledge, this is the first work that introduces the concept of information gain into VSG. Extensive experiments on four public datasets from various fields fully demonstrate that VSG(3)A(2) is more competitive than six state-of-the-art VSG approaches. The MAE, RMSE, and MAPE metrics of prediction models, trained on virtual samples generated by the proposed VSG(3)A(2), are reduced at least by 19.78%, 19.56%, and 20.16% on average than that of the best compared VSG approach, respectively.
