The SeEvo framework revolutionizes evolutionary mechanisms by employing Large Language Models (LLMs) for hyper-heuristic algorithms. Within this system, the Reflector LLM generates prompts to enhance individual performance, while the Generator LLM creates unique Hyper-Driven Responses (HDRs) tailored to specific function signatures, bypassing traditional algorithmic limitations.
SeEvo consists of three pivotal stages: individual co-evolution, self-evolution, and collective evolution. This iterative approach incorporates reflective analysis of parental performances and individual improvements for optimal offspring generation. The workflow allows for adaptive mutation based on macro-level insights gathered from previous evolutionary data.
To support efficient scheduling in the Intelligent Human-Robot Collaboration (HRC) flexible manufacturing system, an intricate pipeline is established for data generation and model fine-tuning. The LLM handles dynamic scheduling tasks, leveraging HDRs from a curated knowledge base. This rapid response capability ensures peak performance under real-world conditions, significantly optimizing production efficiency.
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