提出无需参数的高效代理模型，可低成本发现变量依赖关系。
Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output
- 基于递归链接发现构建无参数代理模型
- 单步差异解比较，2⌈log₂(n)⌉步内必发现缺失依赖
- 边优化边训练，不浪费已付评估成本
代理模型为昂贵问题优化提供低成本评估方案。传统代理仅近似原函数，而最新完美线性代理则以正确方式重构原函数，但仅适用于线性问题。为此，本文提出有限单调完美代理（LyMPuS），克服非线性限制，支持仅差一个变量的解比较。该代理无需参数，可在线训练，无需独立建模步骤；仅使用必要适应度评估，更新时不浪费已有计算成本。同时具备低成本缺失链接检测与低代价链接发现能力，保证在不超过2⌈log₂(n)⌉步内找到缺失依赖。
原文摘要 · Abstract (English)
Surrogates provide a cheap solution evaluation and offer significant leverage for optimizing computationally expensive problems. Usually, surrogates only approximate the original function. Recently, the perfect linear surrogates were proposed that ideally represent the original function. These surrogates do not mimic the original function. In fact, they are another (correct) representation of it and enable a wide range of possibilities, e.g., discovering the optimized function for problems where the direct transformation of the encoded solution into its evaluation is not available. However, many real-world problems can not be represented by linear models, making the aforementioned surrogates inapplicable. Therefore, we propose the Limited Monotonical Perfect Surrogate (LyMPuS), which overcomes this difficulty and enables the comparison of two solutions that differ by a single variable. Our proposition is suitable for limiting the cost of expensive local search procedures. The proposed surrogate is parameterless and can be trained on the fly without any separate surrogate-building step. It uses only the necessary fitness evaluations, and the already-paid costs are not wasted when the model is updated. Finally, it offers low-cost missing-linkage detection and low-cost linkage discovery, guaranteed to find a missing dependency in no more than $2\lceil\log_2(n)\rceil$ steps.
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