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.: Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 334–335. ACM (2019)
Ansari Ardeh, M., Mei, Y., Zhang, M.: A novel genetic programming algorithm with knowledge transfer for uncertain capacitated arc routing problem. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11670, pp. 196–200. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29908-8_16
Ardeh, M.A., Mei, Y., Zhang, M.: Transfer learning in genetic programming hyper-heuristic for solving uncertain capacitated arc routing problem. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 49–56 (2019)
Ardeh, M.A., Mei, Y., Zhang, M.: Genetic programming hyper-heuristics with probabilistic prototype tree knowledge transfer for uncertain capacitated arc routing problems (accepted to appear). In: Proceedings of the IEEE Congress on Evolutionary Computation (2020)
De Lorenzo, A., Bartoli, A., Castelli, M., Medvet, E., Xue, B.: Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence. Genet. Program. Evolvable Mach. 21(1), 181–204 (2019). https://doi.org/10.1007/s10710-019-09363-3
Dinh, T.T.H., Chu, T.H., Nguyen, Q.U.: Transfer learning in genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1145–1151 (2015)
Eliason, S.R.: Maximum Likelihood Estimation : Logic and Practice. Sage, Thousand Oaks (1993)
Feng, L., Ong, Y.S., Lim, M.H., Tsang, I.W.: Memetic search with interdomain learning: a realization between CVRP and CARP. IEEE Trans. Evol. Comput. 19(5), 644–658 (2015)
Gupta, A., Ong, Y., Feng, L.: Insights on transfer optimization: because experience is the best teacher. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 51–64 (2018)
Hasegawa, Y., Iba, H.: Optimizing programs with estimation of bayesian network. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1378–1385 (2006)
Hasegawa, Y., Iba, H.: A bayesian network approach to program generation. IEEE Trans. Evol. Comput. 12(6), 750–764 (2008)
Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Evol. Comput. 23(3), 343–367 (2015)
Liu, Y., Mei, Y.: Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 290–297 (2017)
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: the bayesian optimization algorithm. In: IlliGAL Report, GECCO’99, pp. 525–532. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (1999)
Petrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: Proceedings of IEEE Conference on Evolutionary Computation (1996)
Sałustowicz, R., Schmidhuber, J.: Probabilistic incremental program evolution: stochastic search through program space. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 213–220. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-62858-4_86
Shir, O.M.: Niching in evolutionary algorithms, pp. 1035–1069. Springer, Berlin (2012)
Taylor, M., Whiteson, S., Stone, P.: Transfer learning for policy search methods. In: Proceedings of the International Conference on Machine Learning (2006)
Torrey, L., Shavlik, J.: Transfer learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 242–264. IGI Global, Hershey (2010)
Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Evolving scheduling heuristics via genetic programming with feature selection in dynamic flexible job shop scheduling. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.3024849
Zhang, F., Mei, Y., Zhang, M.: A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 347–355 (2019)
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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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