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» Evolutionary algorithms and matroid optimization problems
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GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
14 years 2 months ago
A stopping criterion based on Kalman estimation techniques with several progress indicators
The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteri...
José Luis Guerrero, Jesús Garc&iacut...
GECCO
2006
Springer
167views Optimization» more  GECCO 2006»
13 years 11 months ago
Estimating the destructiveness of crossover on binary tree representations
In some cases, evolutionary algorithms represent individuals as typical binary trees with n leaves and n-1 internal nodes. When designing a crossover operator for a particular rep...
Luke Sheneman, James A. Foster
GECCO
2008
Springer
130views Optimization» more  GECCO 2008»
13 years 8 months ago
Fitnessless coevolution
We introduce fitnessless coevolution (FC), a novel method of comparative one-population coevolution. FC plays games between individuals to settle tournaments in the selection pha...
Wojciech Jaskowski, Krzysztof Krawiec, Bartosz Wie...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 5 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
2007
Springer
183views Optimization» more  GECCO 2007»
14 years 1 months ago
Screening the parameters affecting heuristic performance
This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Fac...
Enda Ridge, Daniel Kudenko