IEEE Trans Neural Netw Learn Syst. 2014 Mar;25(3):506-19. doi: 10.1109/TNNLS.2013.2275918.
This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.
本文介绍了 Pareto 前沿的主动学习（ALP）算法，这是一种从多目标优化问题中恢复 Pareto 前沿的新方法。ALP 将 Pareto 前沿的识别转化为监督机器学习任务。这种方法可以构建 Pareto 前沿的分析模型。通过主动学习策略，可以减少生成监督信息的计算工作量。具体来说，该模型是从一组信息丰富的训练目标向量中学习得到的。训练目标向量是通过求解不同的标量化问题实例获得的近似 Pareto 最优向量。实验结果表明，与最先进的分布估计算法和广泛使用的遗传技术相比，ALP 可以以较低的计算成本实现更精确的 Pareto 前沿逼近。
