Multilabel classification is a machine learning task in which each instance is assigned to a group of labels. It has gained widespread use in various applications in recent years. Preprocessing, such as feature selection, is an important step in any machine learning or data mining task. It helps to improve the performance of an algorithm and reduce computational time by eliminating highly correlated, irrelevant, and noisy features. A new algorithm called Black Hole, inspired by the phenomenon of black holes, has recently been developed to tackle multi-label classification problems. In this talk, we present a modified version of the Black Hole algorithm that combines it with two genetic algorithm operators: crossover and mutation. The combination of Black Hole and genetic algorithms has the potential to solve multi-label classification problems across a range of domains.