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Pre-trained large language models (LLMs) have powerful capabilities forgenerating creative natural text. Evolutionary algorithms (EAs) can discoverdiverse solutions to complex real-world problems. Motivated by the commoncollective and directionality of text sequence generation and evolution, thispaper illustrates the strong consistency of LLMs and EAs, which includesmultiple one-to-one key characteristics: token embedding and genotype-phenotypemapping, position encoding and fitness shaping, position embedding andselection, attention and crossover, feed-forward neural network and mutation,model training and parameter update, and multi-task learning andmulti-objective optimization. Based on this consistency perspective, existingcoupling studies are analyzed, including evolutionary fine-tuning andLLM-enhanced EAs. Leveraging these insights, we outline a fundamental roadmapfor future research in coupling LLMs and EAs, while highlighting key challengesalong the way. The consistency not only reveals the evolution mechanism behindLLMs but also facilitates the development of evolved artificial agents thatapproach or surpass biological organisms.

A match made in consistency heaven: when large language models meet evolutionary algorithms