  Agents based on Large Language Models (LLMs) are increasingly permeating
various domains of human production and life, highlighting the importance of
aligning them with human values. The current alignment of AI systems primarily
focuses on passively aligning LLMs through human intervention. However, agents
possess characteristics like receiving environmental feedback and
self-evolution, rendering the LLM alignment methods inadequate. In response, we
propose an evolutionary framework for agent evolution and alignment, named
EvolutionaryAgent, which transforms agent alignment into a process of evolution
and selection under the principle of survival of the fittest. In an environment
where social norms continuously evolve, agents better adapted to the current
social norms will have a higher probability of survival and proliferation,
while those inadequately aligned dwindle over time. Experimental results
assessing the agents from multiple perspectives in aligning with social norms
demonstrate that EvolutionaryAgent can align progressively better with the
evolving social norms while maintaining its proficiency in general tasks.
Effectiveness tests conducted on various open and closed-source LLMs as the
foundation for agents also prove the applicability of our approach.
