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» Evolutionary Algorithms for the Satisfiability Problem
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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 1 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
2011
Springer
232views Optimization» more  GECCO 2011»
14 years 7 months ago
Mutation rates of the (1+1)-EA on pseudo-boolean functions of bounded epistasis
When the epistasis of the fitness function is bounded by a constant, we show that the expected fitness of an offspring of the (1+1)-EA can be efficiently computed for any point...
Andrew M. Sutton, Darrell Whitley, Adele E. Howe
158
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ISBRA
2009
Springer
15 years 10 months ago
Untangling Tanglegrams: Comparing Trees by Their Drawings
A tanglegram is a pair of trees on the same set of leaves with matching leaves in the two trees joined by an edge. Tanglegrams are widely used in biology – to compare evolutiona...
Balaji Venkatachalam, Jim Apple, Katherine St. Joh...
COR
2007
134views more  COR 2007»
15 years 3 months ago
Portfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We...
Alberto Fernández, Sergio Gómez
WWW
2009
ACM
16 years 4 months ago
An axiomatic approach for result diversification
Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diver...
Sreenivas Gollapudi, Aneesh Sharma