This chapter introduces the intersection of Explainable Artificial Intelligence (XAI) and Evolutionary Computation (EC), two rapidly evolving fields within artificial intelligence and computational science. As AI systems grow more complex, there is a critical need for transparency and interpretability, particularly in applications where decision-making must be trusted and scrutinized. XAI addresses this by focusing on the transparency and comprehensibility of AI models, enabling users to understand, trust, and effectively deploy AI solutions. In contrast, EC uses principles inspired by natural evolution, such as mutation and selection, to solve complex optimization problems and explore vast search spaces. The convergence of XAI and EC holds significant promise for both fields: XAI can make EC processes and results more interpretable, while EC can be leveraged to optimize and improve XAI methodologies. This chapter explores these synergies and offers insights into how this fusion can lead to more robust, reliable, and understandable AI systems for real-world applications.