AI methods are finding an increasing number of applications, but their often black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) has emerged in response to the need for human understanding of AI models. Evolutionary computation (EC), as a family of powerful optimization and learning tools, has significant potential to contribute to XAI. In this paper, we provide an introduction to XAI and review various techniques in current use for explaining machine learning (ML) models. We then focus on how EC can be used in XAI, and review some XAI approaches which incorporate EC techniques. Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize. Finally, we discuss some open challenges in XAI and opportunities for future research in this field using EC. Our aim is to demonstrate that EC is well-suited for addressing current problems in explainability and to encourage further exploration of these methods to contribute to the development of more transparent and trustworthy ML models and EC algorithms.

人工智能方法的黑箱特性引发了对可追溯性和可信赖性的担忧，解释性人工智能（XAI）领域因此而兴起。本文介绍了XAI，并回顾了目前用于解释机器学习模型的各种技术。重点探讨了如何在XAI中应用进化计算（EC）以及将EC技术融入XAI的一些方法。此外，我们还讨论了在EC本身中应用XAI原则的可行性，研究了这些原则如何揭示EC算法的行为和结果，算法的自动调优，以及算法优化的潜在问题。最后，我们讨论了XAI领域存在的一些挑战和未来研究中利用EC的机会。我们旨在证明EC非常适合解决可追溯性的当前问题，鼓励进一步探索这些方法，以促进更透明和可信赖的机器学习模型和EC算法的发展。

进化计算与可解释人工智能：通往透明智能系统的路线图