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» Metaphor for learning: an evolutionary algorithm
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GECCO
2003
Springer
182views Optimization» more  GECCO 2003»
14 years 27 days ago
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Allan Tucker, Xiaohui Liu, David Garway-Heath
SEAL
2010
Springer
13 years 5 months ago
Dominance-Based Pareto-Surrogate for Multi-Objective Optimization
Abstract. Mainstream surrogate approaches for multi-objective problems build one approximation for each objective. Mono-surrogate approaches instead aim at characterizing the Paret...
Ilya Loshchilov, Marc Schoenauer, Michèle S...
EVOW
2008
Springer
13 years 9 months ago
Artificial Creatures for Object Tracking and Segmentation
We present a study on the use of soft computing techniques for object tracking/segmentation in surveillance video clips. A number of artificial creatures, conceptually, "inhab...
Luca Mussi, Stefano Cagnoni
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 2 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
EC
2006
195views ECommerce» more  EC 2006»
13 years 7 months ago
Automated Global Structure Extraction for Effective Local Building Block Processing in XCS
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
Martin V. Butz, Martin Pelikan, Xavier Llorà...