Self-organizing nets for optimization

IEEE Transactions on Neural Networks
Michele MilanoJürgen Schmidhuber

Abstract

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.

References

May 31, 2001·Evolutionary Computation·N Hansen, A Ostermeier

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Citations

Jul 21, 2007·International Journal of Neural Systems·Yi-Yuan Chen, Kuu-Young Young
Jun 2, 2009·Computers in Biology and Medicine·Vasiliki E MarkakiGeorge K Matsopoulos
Sep 2, 2008·Computer Methods and Programs in Biomedicine·Vasiliki E MarkakiGeorge K Matsopoulos
Sep 10, 2008·IEEE Transactions on Neural Networks·Georg PolzlbauerAndreas Rauber
Feb 7, 2007·IEEE Transactions on Neural Networks·Stefano FerrariN Alberto Borghese
Mar 29, 2006·IEEE Transactions on Neural Networks·Chung-Chian Hsu
Jun 24, 2010·IEEE Transactions on Neural Networks·Lu Xu, Tommy Wai Shing Chow
Nov 16, 2010·IEEE Transactions on Neural Networks·Bin Wang, Hsiao-Dong Chiang

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