Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simplified problems and optimization algorithms that sometimes miss expectations in real-world scenarios. Additionally, trust in the applied algorithm and the solutions it provides is often essential in such settings, but requires an understanding of the search process itself. This leads to evolutionary computation often not being seriously considered by practitioners in many application contexts, among them physics-based modeling. In this article, techniques from evolutionary computation are detailed that can alleviate these problems. First, five real-world physics-based optimization problems are introduced and described by domain experts. For each of these, the requirements for the evolutionary algorithm regarding performance and explainability to increase trust and usability are presented. We found that all domain experts expect fast convergence to a good solution and want some explanations for how the results were formed, while other requirements strongly depend on the respective problem. Finally, we present existing approaches that can be leveraged to improve those aspects of evolutionary algorithms but have to our knowledge never been employed in complex real-world scenarios. This implies a gap between both domains that needs to be closed to exploit the full potential of evolutionary computation.

翻译：进化计算提供了多种工具来解决复杂的真实世界优化问题。然而，研究往往聚焦于规模较小、经过简化的问题，以及有时在真实场景中未达预期的优化算法。此外，在此类应用中，对所用算法及其提供解的信任通常至关重要，但这需要理解搜索过程本身。这导致在许多应用领域中（包括基于物理的建模），从业者往往未认真考虑进化计算。本文详细介绍了能够缓解这些问题的进化计算技术。首先，由领域专家介绍并描述了五个真实世界的基于物理的优化问题。针对每个问题，提出了进化算法在性能与可解释性方面的要求，以增强信任和可用性。我们发现，所有领域专家都期望快速收敛至优质解，并希望获得关于结果形成过程的某种解释，而其他要求则高度依赖于具体问题。最后，我们介绍了现有的一些方法，这些方法可用于改进进化算法的上述方面，但据我们所知，从未在复杂的真实场景中应用过。这表明两个领域之间存在需要弥合的鸿沟，以充分发挥进化计算的潜力。