Abstract
This paper studies how evolutionary algorithms (EA) scale with growing genome size, when used for similarity-based clustering. A simple EA and EAs with problem-dependent knowledge are experimentally evaluated for clustering up to 100,000 objects. We find that EAs with problem-dependent crossover or hybridization scale near-linear in the size of the similarity matrix, while the simple EA, even with problem-dependent initialization, fails at moderately large genome sizes.
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Bach, S.R., Uyar, A.Ş., Branke, J. (2010). Improving the Scalability of EA Techniques: A Case Study in Clustering. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds) Artifical Evolution. EA 2009. Lecture Notes in Computer Science, vol 5975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14156-0_2
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DOI: https://doi.org/10.1007/978-3-642-14156-0_2
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