Reinforcement learning-based multi-objective differential evolution algorithm for feature selection

Feature Selection (FS) can be used to determine the optimal subset of features from a raw dataset by reducing dimensionality and improving accuracy. In this study, a reinforcement learning-based multi-objective differential evolution algorithm (RLMODE) for FS is proposed, which is modeled as a multi-objective optimization problem. First, a reinforcement learning-based offspring generation strategy is designed. The offspring generation strategy is based on the Q-learning framework, which considers each individual in the population as an agent. The dominance relationship between an agent and its predecessor is used to encode the state. A well-chosen action set containing three typical differential evolution mutation operators is available for each agent. The reward is used to update the exclusive Q-table. Moreover, a novel Pareto front (PF) relearning strategy is devised to allow adequate communication between individuals on the PF. The PF relearning strategy reevaluates the potential value of all individuals on the PF as a whole. This promotes the propagation of excellent solutions and improves PF diversity. The proposed RLMODE initially demonstrates its strength in benchmarks. The excellent results for 17 datasets demonstrate RLMODE's merits in terms of dimensionality reduction and accuracy improvement. Therefore, the proposed RLMODE method is a promising FS technique.