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TNN
2010
185views Management» more  TNN 2010»
13 years 14 days ago
An adaptive multiobjective approach to evolving ART architectures
In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, w...
Assem Kaylani, Michael Georgiopoulos, Mansooreh Mo...
GECCO
2009
Springer
113views Optimization» more  GECCO 2009»
13 years 3 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
2005
Springer
196views Optimization» more  GECCO 2005»
13 years 11 months ago
Breeding swarms: a new approach to recurrent neural network training
This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural...
Matthew Settles, Paul Nathan, Terence Soule
GECCO
2000
Springer
13 years 9 months ago
Modeling GA Performance for Control Parameter Optimization
Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control ...
Vincent A. Cicirello, Stephen F. Smith
BMCBI
2006
146views more  BMCBI 2006»
13 years 5 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...