Ensemble Structure of Evolutionary Arti cialNeural
نویسندگان
چکیده
|Evolutionary artiicial neural networks (EANNs) refer to a special class of artiicial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolution can be introduced at various levels of ANNs. It can be used to evolve weights, architectures, and learning parameters and rules. This paper is concerned with the evolution of ANN architectures, where an evolutionary algorithm is used to evolve a population of ANNs. The current practice in evolving ANNs is to choose the best ANN in the last population as the-nal result. This paper proposes a novel approach to form the nal result by combining all the individuals in the last generation in order to make best use of all the information contained in the whole population. This approach regards a population of ANNs as an ensemble of ANNs and use a method to combine them. We have used four simple methods in our computational studies. The rst is the majority voting method. The second and third are linear combination methods over the whole population. The fourth is a linear combination method over a subset of the whole population. The near optimal subset is obtained by a genetic algorithm search. Our experiments have shown that all four methods have produced better results than those produced by the single best individual.
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