.png){kind=icon}
返回
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2022.3230828.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hypervolume contribution is an important concept in evolutionary multiobjective optimization (EMO). It involves hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback is that it is computationally expensive in high-dimensional spaces, which limits its applicability to many-objective optimization. Recently, an R2 indicator variant (i.e., R-2(HVC) indicator) is proposed to approximate the hypervolume contribution. The R-2(HVC) indicator uses line segments along a number of direction vectors for hypervolume contribution approximation. It has been shown that different direction vector sets lead to different approximation qualities. In this article, we propose learning to approximate (LtA), a direction vector set generation method for the R-2(HVC) indicator. The direction vector set is automatically learned from training data. The learned direction vector set can then be used in the R-2(HVC) indicator to improve its approximation quality. The usefulness of the proposed LtA method is examined by comparing it with other commonly used direction vector set generation methods for the R-2(HVC) indicator. Experimental results suggest the superiority of LtA over the other methods for generating high-quality direction vector sets.
Keyword:
Approximation
evolutionary multiobjective optimization (EMO)
hypervolume contribution
hypervolume indicator
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
暂无机构信息
引用论文
Refining Estimates of Bird Collision and Electrocution Mortality at Power Lines in the United States.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏分享收藏分享收藏A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm一种简单快速的基于超体积指标的多目标进化算法
PREAI
分享收藏Few-layered metallic 1T-MoS2/TiO2 with exposed (001) facets: two-dimensional nanocomposites for enhanced photocatalytic activities具有暴露的 (001) 面的少层金属1T-MoS2/TiO2: 用于增强光催化活性的二维纳米复合材料
PREAI
分享收藏
查看更多内容