Effects of imputation strategy on genetic algorithms and neural networks on a binary classification problem

In this paper, we compare the performance of a canonical genetic algorithm (CGA), the Self Adaptive Genetic Algorithm (SAGA), and a feed-forward neural network (FFNN) on a predictive modeling problem with incomplete data. Predictive modeling involves learning relationships between the features and labels of the data points in a dataset. Datasets with missing input values may cause problems for some learning algorithms by biasing the learned models. Imputation refers to techniques for replacing missing data through methods such as statistical probabilities, multivariate analysis, machine learning, or K-nearest neighbors.