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Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

机译：通过利用部分评估实现高度可扩展的进化实验优化

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摘要

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.
机译：众所周知，为了实现进化算法（EA）的有效可扩展性，在变化期间必须正确考虑依赖性（也称为链接）。在灰度盒优化（GBO）设置中，利用关于这些依赖性的先验知识可以大大益处优化。我们特别考虑了部分评估是可能的，这意味着可以有效地评估解决方案的部分修改。这些问题可能是非常困难的，例如不可分离的，多模式和多目标。基因池最佳混合进化算法（Gomea）可以有效利用部分评估，从而显着提高性能和可扩展性。通过与分布算法Amalgam的实际值估计的组合，最近显示Gomea可扩展到实值优化。在本文中，我们明确地介绍了真实值的Gomea（RV-Gomea），并引入了一种通过将Gomea与最着名的真实值eA，协方差矩阵适应演化策略组合（CMA-）构成的新变种es）。将Gomea的两种变体与L-BFG和限量存储器CMA-ES（LM-CMA-ES）进行比较。我们表明，RV-GOMEA的两个变体在GBO设置中实现了出色的性能和可扩展性，这可以比EAS更好地无法有效地利用GBO设置的秩序。

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