Abstract
We present evidence indicating that adding a crossover island greatly improves the performance of a Dynamic Island Model for Adaptive Operator Selection. Two combinatorial optimisation problems are considered: the Onemax benchmark, to prove the concept; and a real-world formulation of the course timetabling problem to test practical relevance. Crossover is added to the recently proposed dynamic island adaptive model for operator selection which considered mutation only. When comparing the models with and without a recombination, we found that having a crossover island significantly improves the performance. Our experiments also provide compelling evidence of the dynamic role of crossover during search: it is a useful operator across the whole search process. The idea of combining different type of operators in a distributed adaptive search model is worth further investigation.
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Available at http://www.cs.qub.ac.uk/itc2007/.
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Acknowledgments
J. A. Soria-Alcaraz would like to thank the Consejo Nacional de Ciencia y tecnologia (CONACyT, México). G. Ochoa would like to thank the University of Angers for hosting and funding a research visit in 2014 that started this collaboration.
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Soria-Alcaraz, J.A., Ochoa, G., Göeffon, A., Lardeux, F., Saubion, F. (2016). Combining Mutation and Recombination to Improve a Distributed Model of Adaptive Operator Selection. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_8
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