Abstract
The Uncertain Capacited Arc Routing Problem (UCARP) is an important variant of arc routing problems that is capable of modelling uncertainties of real-world scenarios. Genetic Programming is utilised to evolve routing policies for vehicles to enable them to make real-time decisions and handle environment uncertainties. However, when the properties of a solved problem change, the trained routing policy becomes ineffective and a new routing policy is needed to be trained. The training process is time-consuming. Nevertheless, by extraction and transfer of some knowledge learned from the previous similar problem, the retraining process can be improved. Transfer learning is a challenging task that entails many aspects to decide about, which can influence the degree by which knowledge transfer can be effective. Consequently, in this paper we propose a parametric framework to formalise these details so that it can facilitate studying different aspects of using transfer learning for handling scenario changes of UCARP. Conducting a large number of experiments, we utilise this framework to analyse different transfer learning mechanisms and demonstrate how it can help with understanding dynamics of knowledge transfer for UCARP.
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Ardeh, M.A., Mei, Y., Zhang, M. (2020). A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_12
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