Metaheuristic design of feedforward neural networks: A review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …
key interest among the researchers and practitioners of multiple disciplines. The FNN …
Particle swarm optimization of deep neural networks architectures for image classification
Deep neural networks have been shown to outperform classical machine learning
algorithms in solving real-world problems. However, the most successful deep neural …
algorithms in solving real-world problems. However, the most successful deep neural …
Comparative analysis of low discrepancy sequence-based initialization approaches using population-based algorithms for solving the global optimization problems
Metaheuristic algorithms have been widely used to solve diverse kinds of optimization
problems. For an optimization problem, population initialization plays a significant role in …
problems. For an optimization problem, population initialization plays a significant role in …
Optimal design of convolutional neural network architectures using teaching–learning-based optimization for image classification
Convolutional neural networks (CNNs) have exhibited significant performance gains over
conventional machine learning techniques in solving various real-life problems in …
conventional machine learning techniques in solving various real-life problems in …
A modified particle swarm optimization algorithm for optimizing artificial neural network in classification tasks
Artificial neural networks (ANNs) have achieved great success in performing machine
learning tasks, including classification, regression, prediction, image processing, image …
learning tasks, including classification, regression, prediction, image processing, image …
Evolutionary artificial neural networks by multi-dimensional particle swarm optimization
In this paper, we propose a novel technique for the automatic design of Artificial Neural
Networks (ANNs) by evolving to the optimal network configuration (s) within an architecture …
Networks (ANNs) by evolving to the optimal network configuration (s) within an architecture …
Advances of metaheuristic algorithms in training neural networks for industrial applications
In recent decades, researches on optimizing the parameter of the artificial neural network
(ANN) model has attracted significant attention from researchers. Hybridization of superior …
(ANN) model has attracted significant attention from researchers. Hybridization of superior …
A hybrid algorithm for artificial neural network training
Artificial neural network (ANN) training is one of the major challenges in using a prediction
model based on ANN. Gradient based algorithms are the most frequent training algorithms …
model based on ANN. Gradient based algorithms are the most frequent training algorithms …
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification
Multilayer perceptron (MLP)(trained with back propagation learning algorithm) takes large
computational time. The complexity of the network increases as the number of layers and …
computational time. The complexity of the network increases as the number of layers and …
Improving the classification accuracy of melanoma detection by performing feature selection using binary Harris hawks optimization algorithm
Out of the various types of skin cancers, melanoma is observed to be the most malignant
and fatal type. Early detection of melanoma increases the chances of survival which …
and fatal type. Early detection of melanoma increases the chances of survival which …