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Knowledge Transfer in Genetic Programming Hyper-heuristics

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Book cover Automated Design of Machine Learning and Search Algorithms

Part of the book series: Natural Computing Series ((NCS))

Abstract

Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as dispatching rules in scheduling and routing policies in vehicle routing. In the real world, it is normal to encounter related problem domains, such as the vehicle routing problem with different objectives, constraints, and/or graph topology. On one hand, different heuristics are required for different problem domains. On the other hand, the knowledge learned from solving previous related problem domains can be helpful for solving the current one. Most existing studies solve different problem domains in isolation, and train/evolve the heuristic for each of them from scratch. In this chapter, we investigate different mechanisms to improve the effectiveness and efficiency of the heuristic retraining by employing knowledge transfer. Specifically, in the context of GPHH, we explored the following two transfer strategies: (1) useful subtrees and (2) importance of terminals, and verified their effectiveness in a case study of the uncertain capacitated arc routing problem.

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Authors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Yi Mei, Mazhar Ansari Ardeh & Mengjie Zhang

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  1. Yi Mei
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  2. Mazhar Ansari Ardeh
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  3. Mengjie Zhang
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Correspondence to Yi Mei .

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Editors and Affiliations

  1. Dept. of Computer Science, University of Pretoria, Pretoria, South Africa

    Nelishia Pillay

  2. School of Computer Science, University of Nottingham, Nottingham, UK

    Rong Qu

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Mei, Y., Ardeh, M.A., Zhang, M. (2021). Knowledge Transfer in Genetic Programming Hyper-heuristics. In: Pillay, N., Qu, R. (eds) Automated Design of Machine Learning and Search Algorithms. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-030-72069-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-72069-8_9

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