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HeurEvo introduces an automated plan–code–component co-evolution framework in which AI agents jointly evolve algorithmic structure, executable implementations, and a shared pool of reusable components for hybrid solver-augmented heuristics. Across combinatorial optimization benchmarks and MIPLIB instances, it matches or surpasses state-of-the-art solvers under tight runtime budgets and sets new results on problems such as hexagon packing.
该论文通过行为分析、表示探查和因果干预，研究了AM、POMO和LEHD等神经组合优化（NCO）路由求解器的内部决策机制，揭示了不同架构在构造解决方案时的差异化模式，例如LEHD依赖当前节点表示进行局部决策、起始节点提供全局导航参考。
The paper proposes a general self-supervised learning framework for combinatorial optimization using Frank-Wolfe methods to handle constraints, with strong empirical results on problems like TSP, Maximum Coverage, and QAP.
This paper introduces a graph-based deep reinforcement learning framework for the one-dimensional bin packing problem, reducing optimality gaps compared to existing methods and enabling zero-shot generalization across instance sizes.
The paper proposes a Dual-GNN Multilevel Coarsening framework to solve the maximum independent set problem efficiently by combining graph neural networks with combinatorial search, achieving near-optimal solutions with significant speedup on benchmark graphs.
This paper presents an unsupervised graph neural network framework for solving the Minimum Dominating Set problem, achieving significant speed improvements and generalization for influence maximization in social networks.
This paper introduces hLLM, a decoding strategy for generative reranking that uses the Hungarian algorithm to achieve single-pass decoding, resulting in a 64x speed-up while maintaining ranking quality.
This paper investigates hybrid quantum-classical neural networks for the vehicle routing problem, finding that encoder feed-forward replacement can reduce model parameters by 56.6% while maintaining near-baseline performance for small to medium instances.
This paper proposes a data-driven framework using graph neural networks to enhance Tabu search by predicting candidate moves' quality, improving efficiency in tactical wireless network design problems.
This paper investigates why standard neural architectures fail to generalize to longer inputs in dynamic programming, using geometric analysis with tropical semiring theory to reveal structural limitations in compositions.
The paper introduces SDDL, a neuro-symbolic framework that improves combinatorial optimization accuracy in resource-constrained language models by translating natural-language problems into formal representations, resulting in higher feasibility rates compared to direct-generation and solver-code baselines.
This paper presents a modified JAMPR deep reinforcement learning model to solve the Pickup and Delivery problem with Capacity and Time Window constraints (CPDPTW), offering fast optimal solutions for small to medium-sized instances and suboptimal solutions for larger ones.
Proposes a model-agnostic framework for assortment optimization using guided discrete diffusion, representing assortments as binary vectors and using reward-guided reverse diffusion to avoid combinatorial enumeration. Shows robustness and high-quality solutions in high-dimensional settings.
This paper introduces PIAC, a framework that improves LLM-based automatic construction of parallel algorithm portfolios by using a potential-gain metric that eliminates the need for reference solutions and by leveraging LLMs to generate diverse instance mutators. It consistently outperforms existing LLM-ACP baselines on TSP and CVRP, achieving up to 19.76% relative improvement.
This paper presents a narrow extension to Leader Reward training for neural combinatorial optimization, replacing the binary leader/non-leader distinction with a stabilized rank signal indexed by a sampling budget K. Tests on TSP-100 show modest improvements in Best-of-8 cost under independent sampling, though the authors make no universal superiority claims.
This paper proposes a method using reservoir computing to recycle computational processes of dynamic programming for combinatorial optimization problems, achieving improved approximation accuracy and reduced computation time on traveling salesman and subset sum problems.
This page describes the spacefilling curve heuristic for generating approximate solutions to the Traveling Salesman Problem, emphasizing its speed, simplicity, and practical applications in routing, logistics, and map drawing.
This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem, demonstrating consistent improvements in collected rewards and optimality gaps over vanilla Transformer architectures.
The paper introduces MILP-Evo, a closed-loop framework that uses LLM-guided program evolution to automatically design white-box MILP solver components (cut selectors and branching rules) by iteratively generating and evaluating candidate programs via end-to-end solver performance on MILP instances.
This paper proposes an oscillatory neural network (ONN) based solver for Sudoku puzzles by formulating them as graph coloring problems, achieving high accuracy on 4x4 and 9x9 puzzles.