Neural Network Training Algorithms This repository contains implementations of neural network training algorithms, including a genetic algorithm-based approach and a gradient descent-based approach.

The results show that back propagation neural network (BP) has a strong non-linear mapping capability and flexible structural design, and it has wide application in many fields, such as function ...

The Non-Dominated Sorting Algorithm-II (Deb et al., 2002) is a well-known variation on the standard genetic algorithm which incorporates the heuristics of elitism, non-dominated sorting and crowding ...

YongSeog Kim, W. Nick Street, Gary J. Russell, Filippo Menczer, Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms, Management Science, Vol ...

Evolutionary optimization (EO) is a technique for finding approximate solutions to difficult or impossible numeric optimization problems. In particular, EO can be used to train a neural network. EO is ...

Brain tumor is one of the most frequently diagnosed types of cancer, and understanding the intricacies of the brain is highly intricate and vital, leading to extensive research efforts. In the realm ...

Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...