# Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization

arXiv: https://arxiv.org/abs/2605.28164 · HTML: https://mlsanity.com/paper/2605.28164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 104 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 21 of 21)

- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [Universal Differential Equations for Scientific Machine Learning](https://mlsanity.com/paper/2001.04385/) (2020)
- [Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport](https://mlsanity.com/paper/2204.06684/) (2022)
- [Differentiable Programming for Differential Equations: A Review](https://mlsanity.com/paper/2406.09699/) (2024)
- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities](https://mlsanity.com/paper/2501.06572/) (2025)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation](https://mlsanity.com/paper/2405.11208/) (2024)
- [A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems](https://mlsanity.com/paper/2410.03205/) (2024)
- [On the definition and importance of interpretability in scientific machine learning](https://mlsanity.com/paper/2505.13510/) (2025)
- [Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables](https://mlsanity.com/paper/2206.14135/) (2022)
- [Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results](https://mlsanity.com/paper/2212.07624/) (2022)
- [Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET](https://mlsanity.com/paper/2305.10569/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Physiological neural representation for personalised tracer kinetic parameter estimation from dynamic PET](https://mlsanity.com/paper/2504.17122/) (2025)
- [Electrical Impedance Tomography based on Genetic Algorithm](https://mlsanity.com/paper/1901.04872/) (2019)
- [Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions](https://mlsanity.com/paper/2010.03783/) (2020)
- [Avoiding Redundant Restarts in Multimodal Global Optimization](https://mlsanity.com/paper/2405.01226/) (2024)
- [A Superlinearly Convergent Evolution Strategy](https://mlsanity.com/paper/2505.10987/) (2025)
- [Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes](https://mlsanity.com/paper/2507.02331/) (2025)

## Abstract (arXiv)

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.