Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Optimal design of neural nets using hybrid algorithms
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Evolutionary Bi-objective Learning with Lowest Complexity in Neural Networks: Empirical Comparisons
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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In this paper we present ALEC (Adaptive Learning by Evolutionary Computation), an automatic computational framework for optimizing neural networks wherein the neural network architecture, activation function, weights and learning algorithms are adapted according to the problem. We explored the performance of ALEC and artificial neural networks for function approximation problems. To evaluate the comparative performance, we used three different well-known chaotic time series. We also report some experimentation results related to convergence speed and generalization performance of four different neural network-learning algorithms. Performances of the different learning algorithms were evaluated when the activation functions and architecture were changed. We further demonstrate how effective and inevitable is ALEC to design a neural network, which is smaller, faster and with a better generalization performance.