Mazhar Ansari Ardeh
ABSTRACT
The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, when the scenario changes, the current routing policy can no longer work effectively, and one has to retrain a new policy for the new scenario which is time consuming. On the other hand, knowledge from solving the previous similar scenarios may be helpful in improving the efficiency of the retraining process. In this paper, we propose different knowledge transfer methods from a source scenario to a similar target scenario and examine them in different settings. The experimental results showed that by knowledge transfer, the retraining process is made more efficient and the same performance can be obtained within a much shorter time without having any negative transfer.
- T. T. H. Dinh, T. H. Chu, and Q. U. Nguyen. Transfer learning in genetic programming. In 2015 IEEE Congress on Evolutionary Computation (CEC), pages 1145--1151, May 2015.Google Scholar
Cross Ref
- M. Iqbal, M. Zhang, and B. Xue. Improving classification on images by extracting and transferring knowledge in genetic programming. In 2016 IEEE Congress on Evolutionary Computation (CEC), pages 3582--3589, July 2016.Google ScholarDigital Library
- Baris Kocer and Ahmet Arslan. Genetic transfer learning. Expert Systems with Applications, 37(10):6997 -- 7002, 2010. Google ScholarDigital Library
- Yuxin Liu, Yi Mei, Mengjie Zhang, and Zili Zhang. Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '17, pages 290--297, New York, NY, USA, 2017. ACM. Google ScholarDigital Library
- Y. Mei, S. Nguyen, B. Xue, and M. Zhang. An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5):339--353, Oct 2017.Google ScholarCross Ref
- Y. Mei, K. Tang, and X. Yao. Capacitated arc routing problem in uncertain environments. In IEEE Congress on Evolutionary Computation, pages 1--8, July 2010.Google ScholarCross Ref
- Sanne Wøhlk. A Decade of Capacitated Arc Routing, pages 29--48. Springer US, Boston, MA, 2008.Google Scholar
Index Terms
- Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem
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