In this paper, we will investigate three different stopping criteria. The first criterion is to use the population of the last generation to form the ensemble; ...
The use of coevolution and the artificial immune system for ensemble learning · Multi-objective optimization of a stacked neural network using an evolutionary ...
The experimental results suggested that using minimum validation fitness of the ensemble as an early stopping criterion performs significantly (with 99% ...
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The other criterion is to stop the evolution when the evolved NN ensemble, i.e., the whole population, is satisfactory according to a certain evaluation. This ...
Missing: Artificial | Show results with:Artificial
The goal of this study was to decide when to terminate training of an artificial neural network ANN. In pursuit of this goal, several characteristics of the ...
Missing: Ensembles Evolutionary
Stopping criteria for ensemble of evolutionary artificial neural networks ... Appl. Soft Comput. 2005. 30 Citations.
The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by ...
5) Stop if the halting criterion is satisfied; otherwise, and go to Step 2). C. Comparison Between Evolutionary Training and Gradient-Based Training. As ...
By Professor Hussein Abbass · Journal articles · Filter by type · Footer menu.
One possible solution to this problem is to combine multiple individuals from the final population into an ensemble. This approach has been successful in super-.
Missing: Stopping Criteria