The rapid growth of nature-inspired metaheuristics has exposed a persistent gap between metaphorical novelty and genuine algorithmic advancement. Motivated by the biophysics of chromatin loop extrusion -- a well-characterized genome-folding process driven by SMC motor complexes and conditional barriers -- we introduce the Loop-Extrusion Linkage (LEL) operator, a structure-learning wrapper that combines online variable-interaction estimation, spectral seriation via the Fiedler vector, and adaptive interval-based subspace search. LEL constructs a sparse interaction graph from successful optimization steps, derives a heuristic one-dimensional variable ordering, and generates overlapping evaluation subsets through stochastic interval growth modulated by learned boundary-crossing probabilities. We evaluate LEL on six synthetic diagnostic functions at d=96 designed to probe specific structural hypotheses -- contiguous blocks, permuted blocks, overlapping windows, banded chains, separable controls, and dense rotated couplings -- across 10^4 and 5 x 10^4 evaluation budgets with 15 independent seeds. Results are assessed via the Wilcoxon signed-rank test with Holm-Bonferroni correction and Vargha-Delaney A12 effect sizes. At 10^4 evaluations, Full LEL achieves the best median log-gap on 3 of 6 functions significantly outperforming all ablations and jSO on the structured tasks. At 5 x 10^4 evaluations, simpler ablations and baselines often surpass the full method, indicating that the adaptive barrier mechanism may over-constrain late-stage search on uniformly partitioned landscapes. The strongest supported finding is that learned spectral ordering consistently improves over graph-only grouping and random variable ordering, suggesting that interaction-graph seriation is the most valuable component of the proposed framework.

翻译：受自然界启发的元启发式算法的快速发展，暴露了隐喻新颖性与真正算法进步之间长期存在的差距。受染色质环挤出（一种由SMC马达复合物和条件屏障驱动的、特征明确的基因组折叠过程）生物物理学的启发，我们提出了环挤出关联算子（LEL），这是一种结构学习包装器，它结合了在线变量交互估计、基于Fiedler向量的谱排序以及自适应区间子空间搜索。LEL从成功优化步骤中构建稀疏交互图，导出启发式一维变量排序，并通过由学习到的边界穿越概率调节的随机区间增长，生成重叠评估子集。我们在d=96的六个合成诊断函数上评估了LEL，这些函数旨在探测特定结构假设——连续块、置换块、重叠窗口、带状链、可分离控制和密集旋转耦合——使用10^4和5×10^4的评估预算，并设置15个独立种子。结果通过Wilcoxon符号秩检验（经Holm-Bonferroni校正）和Vargha-Delaney A12效应量进行评估。在10^4次评估下，完整LEL在6个函数中的3个上取得了最佳中位数log-gap，在结构化任务上显著优于所有消融方法和jSO。在5×10^4次评估下，较简单的消融方法和基线通常优于完整方法，表明自适应屏障机制可能在均匀划分的搜索空间中对后期搜索施加过多约束。得到最强支持的研究发现是，学习到的谱排序始终优于仅基于图的变量分组和随机变量排序，这表明交互图排序是所提出框架中最有价值的组成部分。