Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm

2019 ◽  
pp. 387-392
Author(s):  
Sebastian Litzinger ◽  
Andreas Klos ◽  
Wolfram Schiffmann
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Architecture Optimization

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

scholarly journals Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

Energy and AI ◽  
2020 ◽  
Vol 2 ◽  
pp. 100015 ◽  
Author(s):  
X.J. Luo ◽  
Lukumon O. Oyedele ◽  
Anuoluwapo O. Ajayi ◽  
Olugbenga O. Akinade ◽  
Juan Manuel Davila Delgado ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Electricity Consumption ◽  
Feedforward Neural Network ◽  
Neural Network Architecture

Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function

2013 ◽  
pp. 122-162 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Segmentation ◽  
Network Architecture ◽  
Activation Function ◽  
Color Images ◽  
Neural Network Architecture ◽  
Gray Scale ◽  
Multiobjective Genetic Algorithm ◽  
True Color

The proposed chapter is intended to propose a self supervised image segmentation method by a multi-objective genetic algorithm based optimized MUSIG (OptiMUSIG) activation function with a multilayer self organizing neural network architecture to segment multilevel gray scale intensity images. The multiobjective genetic algorithm based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self organizing neural network architecture is also discussed to segment true color images. These methods are quite efficient enough to overcome the drawbacks of the single objective based OptiMUSIG and ParaOptiMUSIG activation functions to segment gray scale and true color images, respectively. The proposed multiobjective genetic algorithm based optimization methods are applied on three standard objective functions to measure the quality of the segmented images. These functions form the multiple objective criteria of the multiobjective genetic algorithm based image segmentation method.

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