For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued GOMEA (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible.

翻译：对于许多实际优化问题，可以执行部分评估，即改变少数变量对解决方案适应度的影响能够被非常高效地计算。已有研究表明，这种部分评估能被使用连接模型来捕捉问题变量间依赖关系的实值GOMEA（RV-GOMEA）出色地利用。近期，RV-GOMEA引入了条件连接模型，将其最优性能扩展至甚至包含重叠依赖关系的问题。然而，该工作假设依赖结构是先验已知的。基于适应度的连接学习技术此前已被用于在优化过程中检测依赖关系，但仅限于非条件连接模型。在本工作中，我们将基于适应度的连接学习与条件连接建模相结合于RV-GOMEA中。此外，我们提出一种在条件连接模型中建模重叠依赖关系的新方法，以最大化完全互依变量组的联合采样。我们将所得的新型RV-GOMEA变体与其他RV-GOMEA变体及VkD-CMA在12个具有不同程度重叠依赖关系的问题上进行比较。研究发现，新型RV-GOMEA不仅在大多数问题上表现最优，而且优化过程中学习条件连接模型的开销通常可忽略不计。