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126
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NECO
2007
115views more  NECO 2007»
15 years 3 months ago
Training Recurrent Networks by Evolino
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
Jürgen Schmidhuber, Daan Wierstra, Matteo Gag...
136
Voted
SOCO
2010
Springer
15 years 1 months ago
Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics
Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local searc...
Carlos García-Martínez, Manuel Lozan...
128
Voted
CEC
2010
IEEE
15 years 1 months ago
Comparison of GA and PSO performance in parameter estimation of microbial growth models: A case-study using experimental data
In this work we examined the performance of two evolutionary algorithms, a genetic algorithm (GA) and particle swarm optimization (PSO), in the estimation of the parameters of a mo...
Dulce Calcada, Agostinho Rosa, Luis C. Duarte, Vit...
TEC
2011
134views more  TEC 2011»
14 years 10 months ago
Differential Evolution: A Survey of the State-of-the-Art
—Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational step...
Swagatam Das, Ponnuthurai Nagaratnam Suganthan
GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
15 years 9 months ago
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms
Recent research into single–objective continuous Estimation– of–Distribution Algorithms (EDAs) has shown that when maximum–likelihood estimations are used for parametric d...
Peter A. N. Bosman, Dirk Thierens