An evolutionary gravitational search-based feature selection

With recent advancements in data collection tools and the widespread use of intelligent information systems, a huge amount of data streams with lots of redundant, irrelevant, and noisy features are collected and a large number of features (attributes) should be processed. Therefore, there is a growing demand for developing efficient Feature Selection (FS) techniques. Gravitational Search algorithm (GSA) is a successful population-based metaheuristic inspired by Newton's law of gravity. In this research, a novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks. As an NP-hard problem, FS finds an optimal subset of features from a given set. For the proposed wrapper FS method, both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers are used as evaluators. Eighteen well-known UCI datasets are utilized to assess the performance of the proposed approaches. In order to verify the efficiency of proposed algorithms, the results are compared with some popular nature-inspired algorithms (i.e. Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)). The extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.