A match made in consistency heaven: when large language models meet
  evolutionary algorithms
Wang Chao Jiaxuan Zhao Licheng Jiao Lingling Li Fang Liu Shuyuan Yang
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 sequence generation and evolution, this
paper illustrates the strong consistency of LLMs and EAs, which includes
multiple one-to-one key characteristics: token embedding and genotype-phenotype
mapping, position encoding and fitness shaping, position embedding and
selection, attention and crossover, feed-forward neural network and mutation,
model training and parameter update, and multi-task learning and
multi-objective optimization. Based on this consistency perspective, existing
coupling studies are analyzed, including evolutionary fine-tuning and
LLM-enhanced EAs. Leveraging these insights, we outline a fundamental roadmap
for future research in coupling LLMs and EAs, while highlighting key challenges
along the way. The consistency not only reveals the evolution mechanism behind
LLMs but also facilitates the development of evolved artificial agents that
approach or surpass biological organisms.