This paper explores the parallels between large language models (LLMs) and evolutionary algorithms (EAs), highlighting their shared characteristics and potential for interdisciplinary integration. LLMs excel in generating creative text through statistical pattern learning, while EAs are effective in solving complex problems through evolutionary processes. The paper identifies key similarities between LLMs and EAs, including token/individual representation, position encoding/fitness shaping, position embedding/selection, Transformer blocks/reproduction, and model training/parameter adaptation. These parallels suggest that LLMs and EAs can complement each other, with LLMs offering enhanced representation and generation capabilities, and EAs providing efficient optimization and exploration. The paper discusses current research on evolutionary fine-tuning and LLM-enhanced EAs, emphasizing their potential to improve performance and generalization. It also highlights challenges, such as the need for efficient resource management, avoiding catastrophic forgetting, and ensuring security. The integration of LLMs into EAs can enhance their ability to handle complex tasks, particularly in multi-modal and dynamic environments. The paper concludes that the synergy between LLMs and EAs holds promise for advancing artificial intelligence, enabling the development of more adaptive and intelligent systems. Future research should focus on leveraging these parallels to create more effective and efficient interdisciplinary approaches.This paper explores the parallels between large language models (LLMs) and evolutionary algorithms (EAs), highlighting their shared characteristics and potential for interdisciplinary integration. LLMs excel in generating creative text through statistical pattern learning, while EAs are effective in solving complex problems through evolutionary processes. The paper identifies key similarities between LLMs and EAs, including token/individual representation, position encoding/fitness shaping, position embedding/selection, Transformer blocks/reproduction, and model training/parameter adaptation. These parallels suggest that LLMs and EAs can complement each other, with LLMs offering enhanced representation and generation capabilities, and EAs providing efficient optimization and exploration. The paper discusses current research on evolutionary fine-tuning and LLM-enhanced EAs, emphasizing their potential to improve performance and generalization. It also highlights challenges, such as the need for efficient resource management, avoiding catastrophic forgetting, and ensuring security. The integration of LLMs into EAs can enhance their ability to handle complex tasks, particularly in multi-modal and dynamic environments. The paper concludes that the synergy between LLMs and EAs holds promise for advancing artificial intelligence, enabling the development of more adaptive and intelligent systems. Future research should focus on leveraging these parallels to create more effective and efficient interdisciplinary approaches.