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» Evolving neural network ensembles for control problems
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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
14 years 1 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
CEC
2007
IEEE
14 years 2 months ago
Graph design by graph grammar evolution
— Determining the optimal topology of a graph is pertinent to many domains, as graphs can be used to model a variety of systems. Evolutionary algorithms constitute a popular opti...
Martin H. Luerssen, David M. W. Powers
JITECH
2010
116views more  JITECH 2010»
13 years 6 months ago
Design theory for dynamic complexity in information infrastructures: the case of building internet
We propose a design theory that tackles dynamic complexity in the design for Information Infrastructures (IIs) defined as a shared, open, heterogeneous and evolving socio-technica...
Ole Hanseth, Kalle Lyytinen
BMCBI
2006
152views more  BMCBI 2006»
13 years 7 months ago
A two-stage approach for improved prediction of residue contact maps
Background: Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvemen...
Alessandro Vullo, Ian Walsh, Gianluca Pollastri
NN
1998
Springer
13 years 7 months ago
A tennis serve and upswing learning robot based on bi-directional theory
We experimented on task-level robot learning based on bi-directional theory. The via-point representation was used for ‘learning by watching’. In our previous work, we had a r...
Hiroyuki Miyamoto, Mitsuo Kawato