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This work serves as the first exploration on automating design of complex learning-assisted optimization algorithms by regarding MetaBBO's design loop as coding task, where openendedness is introduced into MetaBBO with recursive self-improvement capability of advanced coding agents.
The experimental results demonstrate that RLEA outperforms the previous state-of-the-ar method, achieving a 16.67% higher success rate while significantly reducing runtime errors, and validate that integrating reinforcement learning with LLM-based reasoning is highly effective for automated optimization modeling.
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