Evolutionary Hybrid Optimization for Multi-Robot Task Allocation with LLM Guidance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective multi-robot task allocation (MRTA) remains challenging due to complex combinatorial solution spaces and domain-specific constraints. Traditional evolutionary algorithms typically employ generic genetic operations, which lack domain-specific insights, leading to limited efficiency and suboptimal results. This paper proposes a novel evolutionary optimization framework that explicitly leverages semantic reasoning from a Large Language Model (LLM) to guide evolutionary operators. Our method integrates semantic crossover and mutation-driven explicitly by the LLM's semantic capabilities-with a rigorous derivative-free local optimization approach (Speed-Up Slow-Down, SUSD). Experimental evaluations across two MRTA scenarios demonstrate that our semantic-evolutionary method achieves substantial makes pan improvements and accelerated convergence compared to classical evolutionary methods. These results explicitly highlight the benefits of incorporating semantic knowledge into evolutionary optimization, providing enhanced exploration and exploitation balance. Our framework demonstrates promising potential for broad applicability across various combinatorial optimization challenges beyond MRTA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it