  Pre-trained large language models (LLMs) have powerful capabilities for
generating creative natural text. Evolutionary algorithms (EAs) can discover
diverse solutions to complex real-world problems. Motivated by the common
collective and directionality of text generation and evolution, this paper
illustrates the parallels between LLMs and EAs, which includes multiple
one-to-one key characteristics: token representation and individual
representation, position encoding and fitness shaping, position embedding and
selection, Transformers block and reproduction, and model training and
parameter adaptation. By examining these parallels, we analyze existing
interdisciplinary research, with a specific focus on evolutionary fine-tuning
and LLM-enhanced EAs. Drawing from these insights, valuable future directions
are presented for advancing the integration of LLMs and EAs, while highlighting
key challenges along the way. These parallels not only reveal the evolution
mechanism behind LLMs but also facilitate the development of evolved artificial
agents that approach or surpass biological organisms.
