An evolutionary algorithm for feed-forward neural networks optimization

We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a sp...

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Bibliographic Details
Main Authors: Safi, Youssef, Bouroumi, Abdelaziz
Format: Conference Proceeding
Language:English
Subjects:
artificial neural networks
Classification algorithms
evolutionary algorithms
evolutionary strategies
Glass
machine learning
optimization
Training
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Summary:We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a specific problem. The performance of the proposed algorithm in solving classification and prediction problems is experimentally tested using five real-world benchmark datasets. The experimental results are analyzed and compared to those produced by two other methods using two measures of performance.
DOI:10.1109/ICoCS.2014.7060901