It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.

中文翻译：

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通过利用部分评估实现高度可扩展的进化实值优化

众所周知，要实现进化算法 (EA) 的高效可扩展性，必须在变化过程中适当考虑依赖性（也称为链接）。在灰盒优化 (GBO) 设置中，利用有关这些依赖项的先验知识可以极大地有益于优化。我们特别考虑了可以进行部分评估的设置，这意味着可以有效地评估解决方案的部分修改。此类问题可能非常困难，例如不可分离、多模态和多目标。基因池最优混合进化算法 (GOMEA) 可以有效地利用部分评估，从而显着提高性能和可扩展性。GOMEA 最近被证明可以通过与分布算法 AMaLGaM 的实值估计相结合来扩展到实值优化。在本文中，我们明确地介绍了实值 GOMEA (RV-GOMEA)，并介绍了一个新的变体，该变体是通过将 GOMEA 与可以说是最著名的实值 EA、协方差矩阵适应演化策略 (CMA- ES）。GOMEA 的两种变体都与 L-BFGS 和有限内存 CMA-ES (LM-CMA-ES) 进行了比较。我们表明，RV-GOMEA 的两种变体在 GBO 设置中都实现了出色的性能和可扩展性，这可能比无法有效利用 GBO 设置的 EA 好几个数量级。通过将 GOMEA 与可以说是最著名的实值 EA、协方差矩阵适应演化策略 (CMA-ES) 相结合而构建。GOMEA 的两种变体都与 L-BFGS 和有限内存 CMA-ES (LM-CMA-ES) 进行了比较。我们表明，RV-GOMEA 的两种变体在 GBO 设置中都实现了出色的性能和可扩展性，这可能比无法有效利用 GBO 设置的 EA 好几个数量级。通过将 GOMEA 与可以说是最著名的实值 EA、协方差矩阵适应演化策略 (CMA-ES) 相结合而构建。GOMEA 的两种变体都与 L-BFGS 和有限内存 CMA-ES (LM-CMA-ES) 进行了比较。我们表明，RV-GOMEA 的两种变体在 GBO 设置中都实现了出色的性能和可扩展性，这可能比无法有效利用 GBO 设置的 EA 好几个数量级。