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Human-engineered black-box optimizers have been eclipsed by a recursive dual-agent system that autonomously evolves both the target optimizer codebase and its own meta-design logic from scratch.
A novel learning-assisted planning framework, termed Landscape-Aware Meta Differential Evolution (LAMDE), is proposed to tackle the mentioned limitations and achieves state-of-the-art performance on the tested complex UAV path planning tasks in WSN data collection scenarios.