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аналитика - Neural Combinatorial Optimization
Self-Improvement for Neural Combinatorial Optimization: Bridging Behavior Cloning and Policy Gradient Methods
Bridging behavior cloning and policy gradient methods to simplify training processes in neural combinatorial optimization.
Instance-Conditioned Adaptation Model for Improving Large-scale Generalization of Neural Combinatorial Optimization
The proposed Instance-Conditioned Adaptation Model (ICAM) effectively integrates instance-conditioned information, such as problem scale and node-to-node distances, into both the encoding and decoding processes of a neural combinatorial optimization model, enabling it to achieve state-of-the-art performance on large-scale Traveling Salesman Problem and Capacitated Vehicle Routing Problem instances.
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