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82
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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
15 years 2 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
GECCO
2009
Springer
113views Optimization» more  GECCO 2009»
14 years 8 months ago
Single step evolution of robot controllers for sequential tasks
The generation of robot controllers for a task requiring a sequence of elementary behaviors is still a challenge. If these behaviors are known, intermediate steps can be given to ...
Stéphane Doncieux, Jean-Baptiste Mouret
GECCO
2009
Springer
135views Optimization» more  GECCO 2009»
15 years 4 months ago
Neuroevolutionary reinforcement learning for generalized helicopter control
Helicopter hovering is an important challenge problem in the field of reinforcement learning. This paper considers several neuroevolutionary approaches to discovering robust cont...
Rogier Koppejan, Shimon Whiteson
81
Voted
GECCO
2009
Springer
150views Optimization» more  GECCO 2009»
15 years 4 months ago
Integrating real-time analysis with the dendritic cell algorithm through segmentation
As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusio...
Feng Gu, Julie Greensmith, Uwe Aickelin
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 4 months ago
Three interconnected parameters for genetic algorithms
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
Pedro A. Diaz-Gomez, Dean F. Hougen