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, Fransa, 26 - 28 Ekim 2009, cilt.5975, ss.13-15 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 5975
  • Basıldığı Şehir: Strasbourg
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.13-15
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.