Self-organizing nets for optimization

Michele Milano, Petros Koumoutsakos, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Given some optimization problem and a series of typically expensive trials of solution candidates sampled from a search space, how can we efficiently select the next candidate? We address this fundamental problem by embedding simple optimization strategies in learning algorithms inspired by Kohonen's self-organizing maps and neural gas networks. Our adaptive nets or grids are used to identify and exploit search space regions that maximize the probability of generating points closer to the optima. Net nodes are attracted by candidates that lead to improved evaluations, thus, quickly biasing the active data selection process toward promising regions, without loss of ability to escape from local optima. On standard benchmark functions, our techniques perform more reliably than the widely used covariance matrix adaptation evolution strategy. The proposed algorithm is also applied to the problem of drag reduction in a flow past an actively controlled circular cylinder, leading to unprecedented drag reduction.
Original languageEnglish (US)
Pages (from-to)758-765
Number of pages8
JournalIEEE Transactions on Neural Networks
Issue number3
StatePublished - May 1 2004
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications
  • Computer Science Applications


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