Evolutionary computation (EC) is a powerful optimization technique inspired by natural selection. It's used to find the best solutions to complex problems in fields like robotics and AI. However, as problems become more intricate, traditional EC methods struggle. Enter large language models (LLMs). These AI powerhouses, known for their language processing abilities, are now being explored as a way to supercharge EC. Imagine an LLM that can analyze a problem, understand its nuances, and guide the evolutionary process to find better solutions faster. This is the exciting potential researchers are investigating. LLMs could help select the best evolutionary strategies, design more effective starting populations, and even create entirely new evolutionary operators. This means tackling larger, more complex problems with greater efficiency. One example is using LLMs to process complex data, extracting key features to optimize the input for evolutionary algorithms. Another is using LLMs to generate and optimize algorithms themselves, pushing the boundaries of what's possible. While the integration of LLMs and EC is still in its early stages, the possibilities are vast. From improving human-computer interaction in algorithm design to adapting to dynamic environments, LLMs could revolutionize how we solve complex problems. However, challenges remain. LLMs, like any tool, have limitations. Their performance depends heavily on how they're prompted and the context provided. Furthermore, they might not always outperform traditional methods for highly complex problems. Despite these challenges, the fusion of LLMs and EC represents a promising frontier in AI research, paving the way for smarter algorithms and more efficient problem-solving across various fields.