Exploring the Improvement of Evolutionary Computation via Large Language Models
arxiv(2024)
摘要
Evolutionary computation (EC), as a powerful optimization algorithm, has been
applied across various domains. However, as the complexity of problems
increases, the limitations of EC have become more apparent. The advent of large
language models (LLMs) has not only transformed natural language processing but
also extended their capabilities to diverse fields. By harnessing LLMs' vast
knowledge and adaptive capabilities, we provide a forward-looking overview of
potential improvements LLMs can bring to EC, focusing on the algorithms
themselves, population design, and additional enhancements. This presents a
promising direction for future research at the intersection of LLMs and EC.
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