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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.28164 |
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| _version_ | 1866917538692071424 |
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| author | Stegherr, Helena Heider, Michael Meyer, Nils Thummerer, Tobias Wendler, Thomas Aublin, Pierre Idrobo-Àvila, Ennio Mikelsons, Lars Zaunseder, Sebastian Hähner, Jörg |
| author_facet | Stegherr, Helena Heider, Michael Meyer, Nils Thummerer, Tobias Wendler, Thomas Aublin, Pierre Idrobo-Àvila, Ennio Mikelsons, Lars Zaunseder, Sebastian Hähner, Jörg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28164 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization Stegherr, Helena Heider, Michael Meyer, Nils Thummerer, Tobias Wendler, Thomas Aublin, Pierre Idrobo-Àvila, Ennio Mikelsons, Lars Zaunseder, Sebastian Hähner, Jörg Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2605.28164 |