EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization

• URL: http://arxiv.org/abs/2506.02594v1
• Date: Tue, 03 Jun 2025 08:13:41 GMT
• Title: EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization
• Authors: Ruibo Duan, Yuxin Liu, Xinyao Dong, Chenglin Fan,
• Abstract summary: We introduce EALG (Evolutionary Adrial Generation of Language Model Generators), a novel framework that co-evolutione optimization problem instances and their corresponding solvers using large language models (LLMs)<n>EALG leverages a mutation-based approach that dynamically evolves instance generation procedures to create increasingly difficult problems, while simultaneously adaptive adversarial algorithms through interactions with LLMs guided by algorithmic structure.<n>This work explores a new paradigm for optimization that integrates instance generation with solver design, resulting in state-of-the-art performance.
• Score: 5.575239967310329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating challenging instances is crucial for the evaluation and advancement of combinatorial optimization solvers. In this work, we introduce EALG (Evolutionary Adversarial Generation of Language Model-Guided Generators), a novel framework that automates the co-evolution of optimization problem instances and their corresponding heuristic solvers using large language models (LLMs). EALG leverages a mutation-based adversarial approach that dynamically evolves instance generation procedures to create increasingly difficult problems, while simultaneously synthesizing adaptive heuristic algorithms through interactions with LLMs guided by algorithmic structure. Unlike existing approaches that focus solely on static benchmark creation or manual solver design, EALG provides a seamless pipeline from instance generation to solver synthesis. Experimental results demonstrate that EALG generates significantly harder instances than current benchmarks, and its synthesized solvers generalize effectively across a broad spectrum of combinatorial tasks. This work explores a new paradigm for combinatorial optimization that integrates instance generation with solver design, resulting in state-of-the-art performance.

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