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

• URL: http://arxiv.org/abs/2510.02686v1
• Date: Fri, 03 Oct 2025 03:03:14 GMT
• Title: EvoSpeak: Large Language Models for Interpretable Genetic Programming-Evolved Heuristics
• Abstract summary: EvoSpeak is a novel framework that integrates Genetic (GP) with large language models (LLMs) to enhance efficiency, transparency, and adaptability of evolution.<n>We show that EvoSpeak produces more effectiveobjectives, improves evolutionary efficiency, and delivers human-readable reports that enhance usability.
• Score: 37.03584206625906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners [3.831016688248884]
  Large Language Models (LLMs) achieve increasingly sophisticated performance on complex reasoning tasks.<n>Characterizing emergent reasoning is vital for long-term interpretability and safety.<n>We use a custom variant of Genetic Pareto (GEPA) to systematically optimize prompts for scientific reasoning tasks.
  arXiv  Detail & Related papers  (2026-03-30T05:01:07Z)
• EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation [49.789575209305724]
  Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics.<n>Existing solutions, such as distributed mechanisms or hybrid agent-based model (ABM) integrations, either fail to address inference costs or compromise accuracy and generalizability.<n>We propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation.
  arXiv  Detail & Related papers  (2025-05-11T08:51:56Z)
• Evolutionary thoughts: integration of large language models and evolutionary algorithms [2.3633885460047765]
  Large Language Models (LLMs) have unveiled remarkable capabilities in understanding and generating both natural language and code.<n>We propose an enhanced evolutionary search strategy that enables a more focused exploration of expansive solution spaces.
  arXiv  Detail & Related papers  (2025-05-09T03:32:18Z)
• A Survey on Post-training of Large Language Models [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.<n>These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance.<n>This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures ethical coherence and alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Integration and Adaptation, which
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution [61.80716438091887]
  GenDiE (Generate, Discriminate, Evolve) is a novel self-evolving framework that enhances context faithfulness through fine-grained sentence-level optimization.<n>By treating each sentence in a response as an independent optimization unit, GenDiE effectively addresses the limitations of previous approaches.<n>Experiments on ASQA (in-domain LFQA) and ConFiQA datasets demonstrate that GenDiE surpasses various baselines in both faithfulness and correctness.
  arXiv  Detail & Related papers  (2025-03-03T16:08:33Z)
• Generalization of Medical Large Language Models through Cross-Domain Weak Supervision [0.0]
  We propose the Incremental Curriculum-Based Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical large language models (MLLMs)<n>ICFT combines curriculum-based learning, dual-stage memory coordination, and parameter-efficient fine-tuning to enable a progressive transition from general linguistic knowledge to strong domain-specific expertise.
  arXiv  Detail & Related papers  (2025-02-02T16:05:23Z)
• GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts [15.920623515602038]
  Large Language Models (LLMs) are highly susceptible to the influence of prompt words.<n>This paper proposes GANPrompt, a multi-dimensional LLMs prompt diversity framework based on Generative Adversarial Networks (GANs)<n>The framework enhances the model's adaptability and stability to diverse prompts by integrating GANs generation techniques with the deep semantic understanding capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-08-19T03:13:20Z)
• NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models [35.10729451729596]
  Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) However, expensive training as well as inference remains a significant impediment to their widespread applicability. Inspired by brain neuronal networks, we explore sparsity approaches through the lens of network topology.
  arXiv  Detail & Related papers  (2024-02-28T22:21:47Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges [50.280704114978384]
  Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text.<n> Evolutionary algorithms (EAs) can discover diverse solutions to complex real-world problems.
  arXiv  Detail & Related papers  (2024-01-19T05:58:30Z)
• EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers [67.64162164254809]
  EvoPrompt is a framework for discrete prompt optimization.<n>It borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence.<n>It significantly outperforms human-engineered prompts and existing methods for automatic prompt generation.
  arXiv  Detail & Related papers  (2023-09-15T16:50:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.