用深度核学习增强遗传算法，加速高维空间优化
Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms
- 将遗传算法的生成能力与深度核学习的预测效率结合
- 在分子发现和电池充电优化中显著减少计算开销
- 适合需要快速探索复杂高维空间的研究者
在分子发现、流程优化和供应链管理等领域，复杂高维空间的探索面临巨大挑战。遗传算法（GAs）虽能生成新候选解，但需大量评估导致计算成本高；深度核学习（DKL）虽能高效预测预选结构的行为，却缺乏生成能力。本研究提出一种融合GA生成能力与DKL代理模型效率的新方法，构建了DKL-GA框架，可进一步用于构建贝叶斯优化（BO）流程。通过优化FerroSIM模型验证了该方法的有效性，展示了其在分子发现与电池充电优化等任务中的广泛应用潜力。
原文摘要 · Abstract (English)
Exploration of complex high-dimensional spaces presents significant challenges in fields such as molecular discovery, process optimization, and supply chain management. Genetic Algorithms (GAs), while offering significant power for creating new candidate spaces, often entail high computational demands due to the need for evaluation of each new proposed solution. On the other hand, Deep Kernel Learning (DKL) efficiently navigates the spaces of preselected candidate structures but lacks generative capabilities. This study introduces an approach that amalgamates the generative power of GAs to create new candidates with the efficiency of DKL-based surrogate models to rapidly ascertain the behavior of new candidate spaces. This DKL-GA framework can be further used to build Bayesian Optimization (BO) workflows. We demonstrate the effectiveness of this approach through the optimization of the FerroSIM model, showcasing its broad applicability to diverse challenges, including molecular discovery and battery charging optimization.
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