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ECAL
1995
Springer
13 years 9 months ago
Contemporary Evolution Strategies
After an outline of the history of evolutionary algorithms, a new ( ) variant of the evolution strategies is introduced formally. Though not comprising all degrees of freedom, it i...
Hans-Paul Schwefel, Günter Rudolph
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 4 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
GECCO
2008
Springer
238views Optimization» more  GECCO 2008»
13 years 7 months ago
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values...
Antonio LaTorre, José Manuel Peña, V...
GECCO
2008
Springer
143views Optimization» more  GECCO 2008»
13 years 7 months ago
Application domain study of evolutionary algorithms in optimization problems
This paper deals with the problem of comparing and testing evolutionary algorithms, that is, the benchmarking problem, from an analysis point of view. A practical study of the app...
Pilar Caamaño, Francisco Bellas, José...
IWCLS
2007
Springer
14 years 11 days ago
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...
Larry Bull