Evolutionary Cellular Configurations for Designing Feed-Forward Neural Networks Architectures
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
ADANN: automatic design of artificial neural networks
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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In this paper, we present the experimental works performed to test and explore the performance of our proposed framework: meta-learning evolutionary artificial neural network by means of cellular automata (MLEANN-CA). This framework based on evolutionary computation with direct and indirect encoding methods (cellular automata) for automatic design of optimal artificial neural networks wherein the neural network architecture, activation function, connection weights, and the learning algorithm with its parameters are adapted according to the problem. We used two toolboxes for simulations: NeuroSolutions and NeuroGenetic Optimizer besides two famous chaotic time series. We compared the performance of the proposed MLEANN-CA with the previous MLEANN framework, which used the direct encoding methods, and with the conventional design of ANNs. We demonstrated how effective is the proposed MLEANN-CA framework to obtain a design of feed-forward neural network that is smaller, faster and with better generalization performance.