Although exploratory landscape analysis (ELA) has shown its effectiveness in various applications, most previous studies focused only on low- and moderate-dimensional problems. Thus, little is known about the scalability of the ELA approach for large-scale optimization. In this context, first, this paper analyzes the computational cost of features in the flacco package. Our results reveal that two important feature classes (ela_level and ela_meta) cannot be applied to large-scale optimization due to their high computational cost. To improve the scalability of the ELA approach, this paper proposes a dimensionality reduction framework that computes features in a reduced lower-dimensional space than the original solution space. We demonstrate that the proposed framework can drastically reduce the computation time of ela_level and ela_meta for large dimensions. In addition, the proposed framework can make the cell-mapping feature classes scalable for large-scale optimization. Our results also show that features computed by the proposed framework are beneficial for predicting the high-level properties of the 24 large-scale BBOB functions.
翻译:虽然探索景观分析(ELA)在各种应用中显示了其有效性,但以往的大多数研究都只侧重于中低度问题。因此,对于拉美经济体系大规模优化方法的可缩放性知之甚少。首先,本文件分析了Flacco包中地物的计算成本。我们的结果表明,由于计算成本高,两个重要的地物分类(ela_级别和ela_meta)无法应用于大规模优化。为了提高拉美经济体系方法的可缩放性,本文建议了一个维度削减框架,将低度空间的特征与原先的解决方案空间相比进行计算。我们证明,拟议的框架可以大幅缩短大度的ela_级别和ela_meta的计算时间。此外,拟议的框架可以使细胞绘图地物分类能够用于大规模优化。我们的结果还表明,拟议框架所计算的地物对于预测24个大型BOB函数的高水平特性很有帮助。