Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution

TL;DR

This paper introduces AlphaEvolve, an LLM-guided evolutionary method that discovers a closed-form power allocation solution for multi-target radar tracking, achieving near-optimal accuracy and significant speed improvements.

Contribution

It presents a novel LLM-guided evolutionary search framework that autonomously finds interpretable, closed-form solutions for complex radar resource allocation problems.

Findings

Achieves only 1.51% performance loss compared to optimal solutions.

Provides over 1000x speedup over traditional iterative methods.

Demonstrates robust generalization across various scenarios.

Abstract

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.