Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Classifier fitness based on accuracy
Evolutionary Computation
Fifty Years of Vehicle Routing
Transportation Science
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
IEEE Transactions on Evolutionary Computation
Multi objective learning classifier systems based hyperheuristics for modularised fleet mix problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Predicting optimal size and mix of future transportation fleets is of great importance to defence logistics planners but faces some challenges. Firstly, the future is uncertain because the environment changes constantly and adversaries are highly adaptive. Secondly, optimising a large heterogeneous transport fleet is inherently complex. Heuristic-based optimisation techniques are therefore often applied that provide approximate solutions to support the decision making in such complex problems. However, heuristic-based methods act as black boxes and do not offer insights into the relationships between future scenarios and the solutions found. In this paper, we use an evolutionary rule-based approach to understand these relationships. A multi-objective Learning Classifier System (LCS) is employed to learn interpretable patterns of future scenarios and to associate them with the best performing heuristics under given conditions. To accomplish this, two novel reward functions are introduced that assign credits to classifiers based on the multi-objective performance of the predicted heuristics. Results show that LCS generalises well to relate scenario characteristics with Pareto optimal heuristics.