Markov blanket-embedded genetic algorithm for gene selection

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A Survey on Evolutionary Computation Approaches to Feature Selection (2016, background): “[49], [226], and Zhu and Ong [227] proposed memetic algorithms for feature selection, i.”
Gene selection with guided regularized random forest (2012, methodology): “Feature selection has been widely used in many applications such as gene selection [23, 30, 18] as it can moderate the curse of dimensionality, improve interpretability [10] and avoid the eﬀort to analyze irrelevant or redundant features.”
Genetic programming for feature construction and selection in classification on high-dimensional data (2015, result): “The small size of the resulting feature sets is consistent with previous studies that although gene expression data has a large number of features, only a small number of features (less than 100 for two-class datasets) are relevant to the problems [33].”