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GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 2 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
CEC
2010
IEEE
13 years 10 days ago
A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
— Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key...
Jean Berger, Khaled Jabeur, Abdeslem Boukhtouta, A...
ML
2010
ACM
119views Machine Learning» more  ML 2010»
13 years 7 months ago
A cooperative coevolutionary algorithm for instance selection for instance-based learning
This paper presents a cooperative evolutionary approach for the problem of instance selection for instance based learning. The presented model takes advantage of one of the most r...
Nicolás García-Pedrajas, Juan Antoni...
AE
2001
Springer
14 years 1 months ago
Markov Random Field Modelling of Royal Road Genetic Algorithms
Abstract. Markov Random Fields (MRFs) 5] are a class of probabalistic models that have been applied for many years to the analysis of visual patterns or textures. In this paper, ou...
Deryck F. Brown, A. Beatriz Garmendia-Doval, John ...
IJON
2006
131views more  IJON 2006»
13 years 8 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...