The recently introduced Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be among the state-of-the-art for solving grey-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem. For many real-world optimization problems, the linkage structure is unknown a priori and has to be learned online. Previously published work on RV-GOMEA however demonstrated excellent scalability only when the linkage structure is pre-specified appropriately. The commonly used mutual-information-based metric that is used to a learn linkage structure online in the discrete version of GOMEA did not show as effective in the real-valued domain and did not result in similarly excellent results, especially in a black-box setting. In this thesis, the strengths of RV-GOMEA are combined with a new fitness-based linkage learning approach that is inspired by differential grouping but reduces its computational overhead by an order of magnitude for problems with fewer interactions. The resulting new version of RV-GOMEA achieves scalability similar to when a predefined linkage model is used. Additionally, for the first time, the EDA AMaLGaM, that served as a foundation for RV-GOMEA is outperformed in a black-box setting, where partial evaluations cannot be leveraged.

中文翻译：

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实值基因池优化混合进化算法中基于适应度的链接学习

最近推出的实值基因池优化混合进化算法 (RV-GOMEA) 已被证明是解决灰盒优化问题的最新技术之一，其中可以利用部分评估。核心优势是其有效利用问题的联系结构的能力。对于许多现实世界的优化问题，链接结构是先验未知的，必须在线学习。然而，之前发表的关于 RV-GOMEA 的工作只有在适当预先指定链接结构时才表现出出色的可扩展性。用于在 GOMEA 的离散版本中在线学习链接结构的常用基于互信息的度量在实值域中没有表现出同样有效，并且没有产生同样出色的结果，尤其是在黑盒中环境。在本论文中，RV-GOMEA 的优势与一种新的基于适应度的链接学习方法相结合，该方法受到差分分组的启发，但对于交互较少的问题，其计算开销减少了一个数量级。由此产生的 RV-GOMEA 新版本实现了类似于使用预定义链接模型时的可扩展性。此外，作为 RV-GOMEA 基础的 EDA AMaLGaM 首次在无法利用部分评估的黑盒环境中表现出色。由此产生的 RV-GOMEA 新版本实现了类似于使用预定义链接模型时的可扩展性。此外，作为 RV-GOMEA 基础的 EDA AMaLGaM 首次在无法利用部分评估的黑盒环境中表现出色。由此产生的 RV-GOMEA 新版本实现了类似于使用预定义链接模型时的可扩展性。此外，作为 RV-GOMEA 基础的 EDA AMaLGaM 首次在无法利用部分评估的黑盒环境中表现出色。