Multilayer feedforward networks are universal approximators
Neural Networks
Creating artificial neural networks that generalize
Neural Networks
Training with noise is equivalent to Tikhonov regularization
Neural Computation
Noise injection: theoretical prospects
Neural Computation
Comparative evaluation of genetic algorithm and backpropagation for training neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
Evolutionary product unit based neural networks for regression
Neural Networks
Neural Computing and Applications
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Representation properties of networks: Kolmogorov's theorem is irrelevant
Neural Computation
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
RNN based MIMO channel prediction
Signal Processing
Neuroevolution strategies for episodic reinforcement learning
Journal of Algorithms
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Hybrid training of feed-forward neural networks with particle swarm optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Evolving neural networks using the hybrid of ant colony optimization and BP algorithms
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Neural Network Learning With Global Heuristic Search
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
Hi-index | 12.05 |
This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training - usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation - Evolution Strategy.