Abstract
Developing and managing a general method of solving combinatorial optimisation problems reduces the need for expensive human experts when solving previously unseen variations to common optimisation problems. A hyper-heuristic provides such a method. Each hyper-heuristic has its own strengths and weaknesses and we research how these properties can be managed. We construct and compare simplified versions of two existing hyper-heuristics (adaptive and grammar-based), and analyse how each handles the trade-off between computation speed and quality of the solution. We test the two hyper-heuristics on seven different problem domains using the HyFlex framework. We conclude that both hyper-heuristics successfully identify and manipulate low-level heuristics to generate “good” solutions of comparable quality, but the adaptive hyper-heuristic consistently achieves this in a shorter computational time than the grammar based hyper-heuristic.
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Marshall, R.J., Johnston, M., Zhang, M. (2014). A Comparison between Two Evolutionary Hyper-Heuristics for Combinatorial Optimisation. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_52
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DOI: https://doi.org/10.1007/978-3-319-13563-2_52
Publisher Name: Springer, Cham
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