Self-organizing nets for optimization Export

@article{citeulike:2907156, 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.}, author = {Milano, M. and Koumoutsakos, P. and Schmidhuber, J.}, booktitle = {Neural Networks, IEEE Transactions on}, citeulike-article-id = {2907156}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TNN.2004.826132}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1296701}, doi = {10.1109/TNN.2004.826132}, journal = {Neural Networks, IEEE Transactions on}, keywords = {continuous, covariance, evolution-strategies, metaheuristic, neural-network, self-organization}, number = {3}, pages = {758--765}, posted-at = {2008-06-19 12:39:30}, priority = {2}, title = {Self-organizing nets for optimization}, url = {http://dx.doi.org/10.1109/TNN.2004.826132}, volume = {15}, year = {2004} }

TY - JOUR ID - citeulike:2907156 L3 - citeulike-article-id:2907156 N2 - 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. IS - 3 JF - Neural Networks, IEEE Transactions on EP - 765 TI - Self-organizing nets for optimization VL - 15 T2 - Neural Networks, IEEE Transactions on SP - 758 KW - continuous KW - covariance KW - evolution-strategies KW - metaheuristic KW - neural-network KW - self-organization AU - Milano, M AU - Koumoutsakos, P AU - Schmidhuber, J PY - 2004/// UR - http://dx.doi.org/10.1109/TNN.2004.826132 ER -