分析了特定函数拼接下实现完美分解所需的种群规模。
Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations
- 基于单位数函数拼接，推导出完美分解的种群规模理论估计。
- 实验验证了理论估计的准确性，发现部分问题类型难以分解。
- 为SLL优化器适用性提供判断依据，适合研究者参考。
统计关联学习（SLL）是许多先进优化器的核心组成部分，旨在发现变量间的依赖关系。研究表明，使用SLL的优化器性能高度依赖于SLL生成的问题分解质量。因此，理解哪些问题易于或难以通过SLL进行分解具有实际意义。本文针对特定单位数函数的拼接问题，从理论上估算获得完美分解所需的种群规模，并通过实验验证该估计的准确性。最终，基于该估计，识别出对SLL优化器而言可能较难处理的问题类型。
原文摘要 · Abstract (English)
Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers.
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