An Adaptive Initialization and Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Feature Selection in Classification
Hang Xu · Symmetry · 2025
As a commonly used method in classification, feature selection can be treated as a bi-objective optimization problem, whose objectives are to minimize both the classification error and the number of selected features, suitable for multi-objective evolutionary algorithms (MOEAs) to tackle. However, due to the discrete optimization environment and the increasing number of features, traditional MOEAs could face shortcomings in searching abilities, especially for large-scale or high-dimensional datasets. Thereby, in this work, an adaptive initialization and reproduction-based evolutionary algorithm (abbreviated as AIR) is proposed, specifically designed for addressing bi-objective feature selection in classification. In AIR, an adaptive initialization mechanism (abbreviated as AI) and an adaptive reproduction method (abbreviated as AR) have been both designed by analyzing the characteristics of currently selected solutions in order to improve their search abilities and balance the convergence and diversity performances. Moreover, the designing of adaptive initialization also utilizes the implicit symmetry of solutions generated around some interpolation axes in the objective space. In the experiments, AIR is comprehensively compared with five state-of-the-art MOEAs in a list of 20 real-life classification datasets, with its the statistical performance being overall the best in terms of several indicators.