Abstract
The ability to visualise how algorithm performance varies across the feature space of possible instance, both real and synthetic, is critical to algorithm selection. Generalising algorithm performance, based on learning from a subset of instances, creates a “footprint” in instance space. This paper shows how self-organising maps can be used to visualise the footprint of algorithm performance, and illustrates the approach using a case study from university course timetabling. The properties of the timetabling instances, viewed from this instance space, are revealing of the differences between the instance generation methods, and the suitability of different algorithms.
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Smith-Miles, K., Lopes, L. (2011). Generalising Algorithm Performance in Instance Space: A Timetabling Case Study. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_41
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DOI: https://doi.org/10.1007/978-3-642-25566-3_41
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