Probe Population-Based Initialization and Genetic Pool-Based Reproduction for Evolutionary Bi-Objective Feature Selection
针对高维数据双目标特征选择问题，提出两种通用方法（PPI和GPR）改进进化算法的初始化和繁殖过程，实验表明能显著提升搜索性能。
Feature selection can be treated as a bi-objective optimization problem, if aimed at minimizing both classification error and number of selected features, suitable for multi-objective evolutionary algorithms (MOEAs) to solve. However, traditional MOEAs would encounter setbacks when the number of features explodes to high dimensionality, causing difficulties for searching optimal solutions in large-scale decision space. In this paper, we propose two general methods applicable to integrate with existing MOEA frameworks in addressing bi-objective feature selection, especially for high-dimensional datasets. One based on probe populations for improving initialization is called PPI, and the other based on genetic pools for improving reproduction is called GPR, both aimed at boosting the search ability of MOEAs. Tested on 20 datasets, in terms of four performance metrics (including the computational time), the experimental section can be divided into three parts. First, five state-of-the-art MOEAs are used as baseline algorithms to integrate with PPI and GPR, while the integrated versions are then compared with their own baselines. Second, the PPI method is additionally compared with three representative feature selection initialization methods to further identify its advantages. Third, a complete PPI and GPR based MOEA (termed PGMOEA) is proposed to compare with three cutting-edge evolutionary feature selection algorithms to further position its search ability. In general, it is suggested from the empirical results that either PPI or GPR can significantly improve the overall performance of each integrated MOEA, while adopting both of them takes the most complementary effect.