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» On the Brittleness of Evolutionary Algorithms
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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 9 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
CEC
2010
IEEE
14 years 8 months ago
Differential evolution with ensemble of constraint handling techniques for solving CEC 2010 benchmark problems
Several constraint handling techniques have been proposed to be used with the evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single c...
Rammohan Mallipeddi, Ponnuthurai Nagaratnam Sugant...
GECCO
2011
Springer
232views Optimization» more  GECCO 2011»
14 years 3 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
CEC
2011
IEEE
13 years 12 months ago
Effective ranking + speciation = Many-objective optimization
—Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (th...
Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos...
GECCO
2007
Springer
201views Optimization» more  GECCO 2007»
15 years 6 months ago
A parallel framework for loopy belief propagation
There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them....
Alexander Mendiburu, Roberto Santana, Jose Antonio...