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» Morphing methods in evolutionary design optimization
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ICML
1998
IEEE
14 years 11 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
GECCO
2008
Springer
196views Optimization» more  GECCO 2008»
13 years 12 months ago
ADANN: automatic design of artificial neural networks
In this work an improvement of an initial approach to design Artificial Neural Networks to forecast Time Series is tackled, and the automatic process to design Artificial Neural N...
Juan Peralta, Germán Gutiérrez, Arac...
GECCO
2010
Springer
172views Optimization» more  GECCO 2010»
14 years 3 months ago
Designing better fitness functions for automated program repair
Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few simpl...
Ethan Fast, Claire Le Goues, Stephanie Forrest, We...
GECCO
2008
Springer
183views Optimization» more  GECCO 2008»
13 years 12 months ago
UMDAs for dynamic optimization problems
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designe...
Carlos M. Fernandes, Cláudio F. Lima, Agost...
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 5 months ago
Neural network ensembles for time series forecasting
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Victor M. Landassuri-Moreno, John A. Bullinaria