Deep learning has been successfully extended to meta-learning, where previous solving efforts assist in learning how to optimise future optimisation instances. In recent years, learning to optimise approaches h
In recent trends, machine learning is widely used to support decision-making in various domains and industrial operations. Because of the increasing comple
By drawing multiple samples per training instance, we can learn faster and obtain a stable policy gradient estimator with significantly fewer instances. The proposed training algorithm outperforms the conventional greedy rollout baseline, even when combined with the maximum entropy objective. The ...
In recent years, learning to optimise approaches have shown success in solving TSP problems. However, they focus on one type of TSP instance, where the points are uniformly distributed in Euclidean spaces (easy instances). Such approaches cannot generalise to other embedding spaces that represent ...
This permutation is then applied repeatedly to optimise test problems. Here the concept of heuristic sequence is explicit. However the methodology presented is only suitable for the analysis of short subsequences of small numbers of heuristics. In this research, subsequences of heuristic selections are...