Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning and Intelligent Optimization
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Supervised learning linear priority dispatch rules for job-shop scheduling
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Generalising algorithm performance in instance space: a timetabling case study
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Many heuristic methods have been proposed for the job-shop scheduling problem. Different solution methodologies outperform other depending on the particular problem instance under consideration. Therefore, one is interested in knowing how the instances differ in structure and determine when a particular heuristic solution is likely to fail and explore in further detail the causes. In order to achieve this, we seek to characterise features for different difficulties. Preliminary experiments show there are different significant features that distinguish between easy and hard JSSP problem, and that they vary throughout the scheduling process. The insight attained by investigating the relationship between problem structure and heuristic performance can undoubtedly lead to better heuristic design that is tailored to the data distribution under consideration.