Exploiting diversity of neural ensembles with speciated evolution

S. I. Lee, J. H. Ahn, S. B. Cho

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)


In this paper, we evolve artificial neural networks (ANNs) with speciation and combine them with several methods. In general, an evolving system produces one optimal solution for a given problem. However, we argue that many other solutions exist in the final population, which can improve the overall performance. We propose a new method of evolving multiple speciated neural networks by fitness sharing that helps to optimize multi-objective functions with genetic algorithms, and several combination methods to construct ensembles of ANNs. Experiments with the UCI benchmark datasets show that the proposed methods can produce more speciated ANNs and, thus, improve the performance by combining representative individuals with combination methods.

Original languageEnglish
Number of pages6
Publication statusPublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 2001 Jul 152001 Jul 19


OtherInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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