Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this “super-sampling”: a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.

中文翻译：

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推断未来景观：对局部最优水平进行抽样

局部最优网络 (LON) 之间的连接模式可以为优化设计提供启发式设计。LON 研究主要需要对健身环境进行完整枚举，从而将分析限制在与现实生活情况相比规模较小的问题上。因此，LON 采样算法很重要。在本文中，我们研究了二次分配问题 (QAP) 的 LON 构造算法。使用机器学习，我们使用估计的 LON 特征来预测 QAP 域中使用的竞争性启发式的搜索性能。结果表明，通过使用随机森林回归，LON 构建算法产生的适应度景观特征可以解释几乎所有的搜索方差。我们发现 LON 样本比枚举 LON 与搜索更相关。采样 LON 的适应度水平在搜索预测中的重要性得到体现。不同算法产生的 LON 的特征首次结合在预测中，这种“超级采样”的结果很有希望：预测禁忌搜索成功的模型解释了 99% 的方差。为每种 LON 算法的用例以及将一种算法的开发过程与另一种算法的探索性优化相结合进行了论证。