Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Neural network design and the complexity of learning
Neural network design and the complexity of learning
Neural network learning and expert systems
Neural network learning and expert systems
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Simplifying neural networks by soft weight-sharing
Neural Computation
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Towards designing artificial neural networks by evolution
Applied Mathematics and Computation - Special issue on articficial life and robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Feedforward Neural Network Methodology
Feedforward Neural Network Methodology
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
An Artificial Neural Network Representation for Artificial Organisms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Evolutionary Design of MLP Neural Network Architectures
SBRN '97 Proceedings of the 4th Brazilian Symposium on Neural Networks (SBRN '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
An Evolutionary Approach for Tuning Artificial Neural Network Parameters
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
A novel pruning algorithm for self-organizing neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
Fault diagnosis on bottle filling plant using genetic-based neural network
Advances in Engineering Software
Self-calming of a random network of dendritic neurons
Neurocomputing
The criticality of spare parts evaluating model using artificial neural network approach
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Artificial Intelligence Review
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Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks. for these tasks.