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

Researchers identify a significant gap between evolutionary computation research and real-world physics-based optimization applications. Domain experts consistently require fast convergence and algorithm explainability, but existing evolutionary algorithm techniques remain underutilized in complex practical scenarios due to trust and performance concerns.

Evolutionary algorithms represent a powerful computational approach for solving complex optimization problems, yet their adoption in real-world physics-informed applications remains limited despite demonstrated potential. The research bridges an important gap by documenting requirements from domain experts working on five distinct physics-based optimization challenges, revealing that practitioners prioritize two universal needs: rapid convergence to quality solutions and transparency in how algorithms arrive at results. This explainability requirement reflects a broader trend across technical fields where black-box solutions face resistance in high-stakes environments where understanding decision-making processes is essential for validation, debugging, and regulatory compliance. The study identifies existing techniques within evolutionary computation that could address these concerns but have never been systematically applied to complex real-world scenarios. For the AI and optimization research communities, this work highlights a critical implementation gap—the distance between what algorithms can theoretically achieve and what practitioners will actually adopt. The barrier to adoption extends beyond pure performance metrics; stakeholders need algorithmic transparency to build confidence in solutions, particularly in physics-based applications where incorrect results carry tangible consequences. The research suggests that bridging this gap requires not just algorithmic innovation but also better communication and integration of explainability mechanisms into standard evolutionary computation frameworks. Practitioners seeking to deploy optimization solutions in physics-informed domains should prioritize algorithm selections that offer both convergence speed and interpretable search processes, while researchers should focus on real-world validation rather than simplified benchmark problems.

• →Domain experts universally demand fast convergence and explainability from evolutionary algorithms, yet these aspects remain underemphasized in academic research.
• →A significant gap exists between theoretical evolutionary computation techniques and their practical application in complex physics-based optimization problems.
• →Algorithm transparency and interpretability are critical for practitioner trust, particularly in high-stakes physics-informed applications.
• →Existing explainability techniques in evolutionary computation could address real-world needs but have not been systematically deployed in complex scenarios.
• →Bridging research-practice gaps requires focusing on real-world validation and integrating explainability into standard evolutionary algorithm frameworks.