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» Incremental evolution strategy for function optimization
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98
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GECCO
2010
Springer
140views Optimization» more  GECCO 2010»
15 years 2 months ago
Investigating the impact of sequential selection in the (1, 4)-CMA-ES on the noiseless BBOB-2010 testbed
This paper investigates the impact of sequential selection, a concept recently introduced for Evolution Strategies (ESs). Sequential selection performs the evaluations of the diļ¬...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 5 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
87
Voted
GECCO
2007
Springer
138views Optimization» more  GECCO 2007»
15 years 6 months ago
Reducing the space-time complexity of the CMA-ES
A limited memory version of the covariance matrix adaptation evolution strategy (CMA-ES) is presented. This algorithm, L-CMA-ES, improves the space and time complexity of the CMA-...
James N. Knight, Monte Lunacek
GECCO
2007
Springer
215views Optimization» more  GECCO 2007»
15 years 6 months ago
Estimating the spectral sensitivity of a digital sensor using calibration targets
A digital sensor which is used inside a digital camera usually responds to a range of wavelengths. The response of the sensor is proportional to the product of the irradiance fall...
Marc Ebner
PPSN
2004
Springer
15 years 6 months ago
LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation
Abstract. Evolution Strategies, Evolutionary Algorithms based on Gaussian mutation and deterministic selection, are today considered the best choice as far as parameter optimizatio...
Anne Auger, Marc Schoenauer, Nicolas Vanhaecke