Improving the Scalability of EA Techniques: A Case Study in Clustering


Bach S. R. , Uyar A. Ş. , Branke J.

9th International Conference on Evolution Artificial, Strasbourg, France, 26 - 28 October 2009, vol.5975, pp.13-15 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 5975
  • City: Strasbourg
  • Country: France
  • Page Numbers: pp.13-15

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.