Evolutionary Hybrid Optimization for Multi-Robot Task Allocation with LLM Guidance
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".