Enabling Population-Based Architectures for Neural Combinatorial Optimization

arXiv:2601.08696 · cs.NE, cs.LG · Submitted 2026-01-13 · Read on arXiv

cs.NE, cs.LG

Submitted: 2026-01-13

Updated: 2026-09-14

Comments: Accepted manuscript. Published in IEEE Transactions on Evolutionary Computation. DOI: 10.1109/TEVC.2026.3730516. Revised version incorporating the changes made during peer review

Journal ref: IEEE Transactions on Evolutionary Computation, 2026

DOI: 10.1109/TEVC.2026.3730516

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Combinatorial Optimization — 34× in this paper · explained in 2 episodes
• Neural Combinatorial Optimization — 13× in this paper
• Neural Constructive — 11× in this paper
• Embeddings — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Hamming Distance — 8× in this paper · explained in 2 episodes
• Neural Improvement — 8× in this paper
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Maximum Cut — 7× in this paper
• Maximum Independent Set — 7× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Genetic Algorithm — 5× in this paper
• Generalization — 4× in this paper · explained in 13 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Graph Neural Network — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Particle Swarm Optimization — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• GNNs — 1× in this paper · explained in 2 episodes
• Graph Neural Network (GNN) — 1× in this paper · explained in 3 episodes
• Graph Transformer — 1× in this paper
• Hypergraph — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Neural Combinatorial Optimization with Reinforcement Learning
• Self-Improved Learning for Scalable Neural Combinatorial Optimization
• Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
• Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration
• Memory-Enhanced Neural Solvers for Routing Problems
• Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms
• PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization
• A Generalization of Transformer Networks to Graphs
• A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
• A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization

Related papers

• Evolutionary Ensemble of Agents
• Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
• Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
• Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
• Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
• S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning