Efficient natural evolution strategies Export

@inproceedings{SunGECCO2009EfficientNES, abstract = {Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.}, address = {New York, NY, USA}, author = {Sun, Yi and Wierstra, Daan and Schaul, Tom and Schmidhuber, Juergen}, booktitle = {GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation}, citeulike-article-id = {5766347}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1569901.1569976}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1569901.1569976}, doi = {10.1145/1569901.1569976}, isbn = {978-1-60558-325-9}, keywords = {distribution, ea, es, estimation, gaussian, gradient, mutation, natural}, location = {Montreal, Qu\'{e}bec, Canada}, pages = {539--546}, posted-at = {2009-09-10 09:52:44}, priority = {2}, publisher = {ACM}, title = {Efficient natural evolution strategies}, url = {http://dx.doi.org/10.1145/1569901.1569976}, year = {2009} }

TY - CONF ID - SunGECCO2009EfficientNES L3 - citeulike-article-id:5766347 N2 - Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks. CY - Montreal, Québec, Canada SN - 978-1-60558-325-9 AD - New York, NY, USA EP - 546 TI - Efficient natural evolution strategies PB - ACM T2 - GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation SP - 539 KW - distribution KW - ea KW - es KW - estimation KW - gaussian KW - gradient KW - mutation KW - natural AU - Sun, Yi AU - Wierstra, Daan AU - Schaul, Tom AU - Schmidhuber, Juergen PY - 2009/// UR - http://dx.doi.org/10.1145/1569901.1569976 ER -