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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
8 years 8 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
ESANN
2008
8 years 11 months ago
Similarities and differences between policy gradient methods and evolution strategies
Natural policy gradient methods and the covariance matrix adaptation evolution strategy, two variable metric methods proposed for solving reinforcement learning tasks, are contrast...
Verena Heidrich-Meisner, Christian Igel
EWRL
2008
9 years 22 hour ago
Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem
Two variable metric reinforcement learning methods, the natural actor-critic algorithm and the covariance matrix adaptation evolution strategy, are compared on a conceptual level a...
Verena Heidrich-Meisner, Christian Igel
GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
9 years 1 months ago
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
First, the covariance matrix adaptation (CMA) with rankone update is introduced into the (1+1)-evolution strategy. An improved implementation of the 1/5-th success rule is propose...
Christian Igel, Thorsten Suttorp, Nikolaus Hansen
EMO
2006
Springer
172views Optimization» more  EMO 2006»
9 years 1 months ago
Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) combines a mutation operator that adapts its search distribution to the underlying optimization prob...
Christian Igel, Thorsten Suttorp, Nikolaus Hansen
GECCO
2010
Springer
195views Optimization» more  GECCO 2010»
9 years 2 months ago
Improved step size adaptation for the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is an evolutionary algorithm for continuous vector-valued optimization. It combines indicator-based...
Thomas Voß, Nikolaus Hansen, Christian Igel
PPSN
2004
Springer
9 years 3 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
CEC
2005
IEEE
9 years 3 months ago
Dynamic niching in evolution strategies with covariance matrix adaptation
Abstract- Evolutionary Algorithms (EAs) have the tendency to converge quickly into a single solution in the search space. However, many complex search problems require the identiï¬...
Ofer M. Shir, Thomas Bäck
CEC
2008
IEEE
9 years 4 months ago
Scalarization versus indicator-based selection in multi-objective CMA evolution strategies
Abstract—While scalarization approaches to multicriteria optimization become infeasible in the case of many objectives, for few objectives the benefits of populationbased method...
Thomas Voß, Nicola Beume, Günter Rudolp...
EVOW
2010
Springer
9 years 4 months ago
Investigating the Local-Meta-Model CMA-ES for Large Population Sizes
For many real-life engineering optimization problems, the cost of one objective function evaluation can take several minutes or hours. In this context, a popular approach to reduce...
Zyed Bouzarkouna, Anne Auger, Didier Yu Ding
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