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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 6 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
FGCS
2010
119views more  FGCS 2010»
13 years 7 months ago
Characterizing fault tolerance in genetic programming
Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of computation. ...
Daniel Lombraña Gonzalez, Francisco Fern&aa...
GECCO
2010
Springer
248views Optimization» more  GECCO 2010»
14 years 5 days ago
Integrating decision space diversity into hypervolume-based multiobjective search
Multiobjective optimization in general aims at learning about the problem at hand. Usually the focus lies on objective space properties such as the front shape and the distributio...
Tamara Ulrich, Johannes Bader, Eckart Zitzler
GECCO
2008
Springer
153views Optimization» more  GECCO 2008»
13 years 9 months ago
G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets
An open problem in multiobjective optimization using the Pareto optimality criteria, is how to evaluate the performance of different evolutionary algorithms that solve multi– o...
Giovanni Lizárraga Lizárraga, Arturo...
EUROGP
2006
Springer
138views Optimization» more  EUROGP 2006»
14 years 13 days ago
Evolving Crossover Operators for Function Optimization
Abstract. A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Pro...
Laura Diosan, Mihai Oltean