一句话：揭示软件产品线最优特征选择的多目标二值整数线性规划本质，并提出可线性时间生成完整Pareto前沿的高效方法。
本文研究软件产品线中最优特征选择问题。作者首先指出该问题本质上属于多目标二值整数线性规划，而非仅能依赖基于指标的进化算法。随后针对不同规模问题提出三类数学规划方法：小规模（约少于100个特征）采用两种已有方法求全部精确解；中大规模（约多于100个特征）提出一种线性时间复杂度方法，可生成整个Pareto前沿的表示。实验表明，与IBEA及其结合差分进化的改进版IBED相比，所提方法能在相近或更少时间内找到显著更多的非支配解。
The optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study we first expose the mathematical nature of this problem --- multi-objective binary integer linear programming. Then, we implement/propose three mathematical programming approaches to solve this problem at different scales. For small-scale problems (roughly less than 100 features), we implement two established approaches to find all exact solutions. For medium-to-large problems (roughly, more than 100 features), we propose one efficient approach that can generate a representation of the entire Pareto front in linear time complexity. The empirical results show that our proposed method can find significantly more non-dominated solutions in similar or less execution time, in comparison with IBEA and its recent enhancement (i.e., IBED that combines IBEA and Differential Evolution).
DOI 原文 ·
@inproceedings{paperbot4459,
title = {Multi-objective integer programming approaches for solving optimal feature selection problem},
author = {Yinxing Xue and Yan-Fu Li},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180257}
}