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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.01794 |
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| _version_ | 1866918479441952768 |
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| author | Hou, Zhenkang Pu, Wenqiang Yan, Junkun Zhou, Rui Liu, Hongwei |
| author_facet | Hou, Zhenkang Pu, Wenqiang Yan, Junkun Zhou, Rui Liu, Hongwei |
| contents | Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a deterministic constraint-satisfying transformation. Extensive experiments demonstrate that the discovered closed-form solution achieves near-optimal tracking accuracy (average relative performance loss of only $1.51\%$), reliable generalization across diverse scenarios and target counts, and over three orders of magnitude speedup compared to conventional iterative solvers. These results highlight the potential of LLM-guided symbolic search to revolutionize not only radar resource management but also broader classes of engineering optimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01794 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution Hou, Zhenkang Pu, Wenqiang Yan, Junkun Zhou, Rui Liu, Hongwei Signal Processing Artificial Intelligence Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a deterministic constraint-satisfying transformation. Extensive experiments demonstrate that the discovered closed-form solution achieves near-optimal tracking accuracy (average relative performance loss of only $1.51\%$), reliable generalization across diverse scenarios and target counts, and over three orders of magnitude speedup compared to conventional iterative solvers. These results highlight the potential of LLM-guided symbolic search to revolutionize not only radar resource management but also broader classes of engineering optimization problems. |
| title | Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2605.01794 |