QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design

• URL: http://arxiv.org/abs/2607.11916v1
• Date: Mon, 06 Jul 2026 07:35:23 GMT
• Title: QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design
• Abstract summary: We propose a multi-objective framework that integrates Quality-Diversity optimization with LLM-driven search.<n>We show that QDEvo significantly outperforms state-of-the-art methods in both Hypervolume and In Generational Distances.<n>Our framework enables the discovery of metrics that are simultaneously high-performing, computationally efficient, and semantically diverse.
• Score: 2.942531574809674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in combinatorial optimization. However, existing approaches suffer from mode collapse, converging to homogeneous populations that lack semantic diversity and fail to explore the full algorithmic space. We propose Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-driven heuristic search, maintaining an unbounded archive of semantically diverse algorithms using pre-trained code embeddings and incorporating hierarchical self-reflection to guide the evolutionary process. Extensive experiments across standard benchmarks and real-world industrial applications demonstrate that QDEvo significantly outperforms state-of-the-art methods in both Hypervolume and Inverted Generational Distance metrics. Our framework enables the discovery of heuristics that are simultaneously high-performing, computationally efficient, and semantically diverse, providing practitioners with a rich portfolio of solutions for complex optimization problems.

Related papers

• Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLMs [69.91297564489464]
  EvoStage is a novel evolutionary paradigm that bridges the gap between the rigorous demands of industrial-scale algorithm design and the LLM-based algorithm design methods.<n>EvoStage decomposes the algorithm design process into sequential, manageable stages and integrates real-time intermediate feedback to iteratively refine algorithm design directions.<n>When deployed on a commercial-grade 3D chip placement tool, EvoStage significantly surpasses the original performance metrics, achieving record-breaking efficiency.
  arXiv  Detail & Related papers  (2026-03-09T05:13:44Z)
• CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models [18.725552141930542]
  Category Driven Automatic Algorithm Design with Large Language Models (CDEoH)<n>We propose Category Driven Automatic Algorithm Design with Large Language Models (CDEoH)<n>We show that CDEoH effectively mitigates convergence toward a single evolutionary direction, significantly enhancing evolutionary stability and achieving consistently superior average performance across tasks and scales.
  arXiv  Detail & Related papers  (2026-03-08T06:20:05Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design [124.54166764570972]
  Combinatorial optimization problems are traditionally tackled with handcrafted algorithms.<n>Recent progress has highlighted the potential of automatics design powered by large language models.<n>We propose the Experience-Evolution Reflective Co-Guided of Prompt and Heuristics (EvoPH) for automatic algorithm design.
  arXiv  Detail & Related papers  (2025-09-29T09:24:09Z)
• REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models [39.85828629779943]
  Multi-objective optimization is fundamental in complex decision-making tasks.<n>Recent advances in Large Language Models (LLMs) offer enhanced explainability, adaptability, and reasoning.<n>This work proposes Reflective Evolution of Multi-objective Heuristics (REMoH), a novel framework integrating NSGA-II with LLM-based generation.
  arXiv  Detail & Related papers  (2025-06-09T13:38:28Z)
• CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design [11.639825726501659]
  Large language models (LLMs) can autonomously discover high-performings at a fraction of the traditional cost.<n>We propose a hybrid framework that combines verbal and numerical guidance.<n>Our method outperforms state-of-the-art (SOTA) baselines across various optimization tasks.
  arXiv  Detail & Related papers  (2025-05-18T07:48:47Z)
• Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization [0.0]
  We introduce Vector Quantized-Elites (VQ-Elites), a novel Quality-Diversity algorithm that autonomously constructs a structured behavior space grid.<n>At the core of VQ-Elites is the integration of Vector Quantized Variational Autoencoders, which enables the dynamic learning of behavior descriptors.<n>We validate VQ-Elites on robotic arm pose-reaching, mobile robot space-covering, and MiniGrid exploration tasks.
  arXiv  Detail & Related papers  (2025-04-10T18:23:19Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Multi-objective Evolution of Heuristic Using Large Language Model [29.337470185034555]
  We model the search as a multi-objective optimization problem and consider introducing additional practical criteria beyond optimal performance.<n>We propose the first multi-objective search framework, Multi-objective Evolution of Heuristic (MEoH)
  arXiv  Detail & Related papers  (2024-09-25T12:32:41Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision [74.9260745577362]
  This paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC) principles. We construct three propagative modules to effectively solve the optimization models with flexible combinations. Experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.
  arXiv  Detail & Related papers  (2020-12-10T03:24:53Z)

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.