# EvoSpeak: Large Language Models for Interpretable Genetic Programming-Evolved Heuristics

arXiv: https://arxiv.org/abs/2510.02686 · HTML: https://mlsanity.com/paper/2510.02686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution](https://mlsanity.com/paper/2402.01145/) (2024)
- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers](https://mlsanity.com/paper/2309.08532/) (2023)
- [Large Language Models as Evolutionary Optimizers](https://mlsanity.com/paper/2310.19046/) (2023)
- [Multi-objective Evolution of Heuristic Using Large Language Model](https://mlsanity.com/paper/2409.16867/) (2024)
- [Evolving Code with A Large Language Model](https://mlsanity.com/paper/2401.07102/) (2024)
- [Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models](https://mlsanity.com/paper/2407.10873/) (2024)
- [When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges](https://mlsanity.com/paper/2401.10510/) (2024)
- [Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance](https://mlsanity.com/paper/2304.06715/) (2023)
- [LLM-Driven Instance-Specific Heuristic Generation and Selection](https://mlsanity.com/paper/2506.00490/) (2025)
- [Explaining Genetic Programming Trees using Large Language Models](https://mlsanity.com/paper/2403.03397/) (2024)
- [Learning Interpretable Low-dimensional Representation via Physical Symmetry](https://mlsanity.com/paper/2302.10890/) (2023)
- [Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons](https://mlsanity.com/paper/2411.10697/) (2024)

## Built on by (most cited first, 1 of 1)

- [Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling](https://mlsanity.com/paper/2607.27698/) (2026)

## Abstract (arXiv)

Genetic programming (GP) has demonstrated strong effectiveness in evolving tree-structured heuristics for complex optimization problems. Yet, in dynamic and large-scale scenarios, the most effective heuristics are often highly complex, hindering interpretability, slowing convergence, and limiting transferability across tasks. To address these challenges, we present EvoSpeak, a novel framework that integrates GP with large language models (LLMs) to enhance the efficiency, transparency, and adaptability of heuristic evolution. EvoSpeak learns from high-quality GP heuristics, extracts knowledge, and leverages this knowledge to (i) generate warm-start populations that accelerate convergence, (ii) translate opaque GP trees into concise natural-language explanations that foster interpretability and trust, and (iii) enable knowledge transfer and preference-aware heuristic generation across related tasks. We verify the effectiveness of EvoSpeak through extensive experiments on dynamic flexible job shop scheduling (DFJSS), under both single- and multi-objective formulations. The results demonstrate that EvoSpeak produces more effective heuristics, improves evolutionary efficiency, and delivers human-readable reports that enhance usability. By coupling the symbolic reasoning power of GP with the interpretative and generative strengths of LLMs, EvoSpeak advances the development of intelligent, transparent, and user-aligned heuristics for real-world optimization problems.