arXiv cs.AI· Corrado Calamita, Matteo Mendicini, Luca Ferrari, Riccardo Bertamini·· 11 小时前AI 评分18
从进入到留存：质量-多样性搜索中如何预测持久后代
From Admission to Retention: Predicting Durable Offspring in Quality-Diversity Search
AI 导读
研究在无结构质量-多样性（QD）存档中，个体繁殖前可获得的信号能否区分仅进入存档与多代后仍被保留的后代。在平面机械臂基准和两个机器人运动任务上，同一信号常与进入和留存呈相反关联，谱系深度是最明显的此类信号，其留存判别力随种群容量增大而增强。但将父代选择偏向与存活关联最强的信号并未改善搜索：中等偏置与均匀采样持平，强偏置降低 QD-score。
正文 · 原文
Abstract:Automated failure discovery can be framed as a diversity problem: the objective is to expose distinct ways a system fails rather than a single critical adversarial case. Yet uniform parent sampling provides no way to allocate search effort toward individuals likely to produce durable archive contributions. We study this credit assignment problem in an unstructured Quality-Diversity (QD) archive, testing whether signals available before an individual has reproduced distinguish offspring that merely enter the archive from those still retained generations later. Across a planar-arm benchmark and two robotic-locomotion tasks, the same signals are often associated with entry and persistence in opposite directions. In particular, lineage depth is the clearest such signal, and its persistence discrimination grows with population capacity, which is associated with lower turnover. Biasing parent selection toward the signals with the strongest measured association to survival nevertheless does not improve search: moderate bias matches uniform sampling, and strong bias reduces QD-score.
| Comments: | Accepted at the NeurIPS 2026 Workshop on Self-Evolving Diversity-Driven Search for Robust AI Systems (EvoRobust) |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06522 [cs.NE] |
| (or arXiv:2611.06522v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06522 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Corrado Calamita [view email]
[v1]
Wed, 4 Nov 2026 13:47:40 UTC (359 KB)
来源：arXiv cs.AI · arxiv.org