A robust evolutionary algorithm for training neural networks

Jinn-Moon Yang*, Cheng Yan Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.

Original languageEnglish
Pages (from-to)214-230
Number of pages17
JournalNeural Computing and Applications
Volume10
Issue number3
DOIs
StatePublished - 1 Jan 2001

Keywords

  • Adaptive mutations
  • Evolutionary algorithm
  • Family competition
  • Multiple mutations
  • Neural networks

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