Optimization Using Artificial Neural Network And Genetic Algorithm

Artificial intelligent tools like genetic algorithm, artificial neural network ANN and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer

Artificial neural network is a simplified model of a biological neural network that consists of a set of artificial neurons interacting with each other. The basic processing unit that collects inputs transforming it and gives an output signal to other neurons or outside the network is a neuron.

To model the process and achieve the desired output for the development of simulator of self-propelled machinery, artificial neural network ANN technique was employed. The simulator was tested using a range of control lever positions 0-23 and engine speeds 1600 and 2000 rpm.

Genetic Algorithms GA Genetic algorithms are employed as the evolutionary mechanism in neuroevolution. They involve creating a population of neural networks, evaluating their performance on a

Additionally, the optimization process often involves the evaluation of fitness functions, which can be computationally expensive. This can limit the practicality of using genetic algorithms and neural networks for real-time applications or scenarios with limited computational resources. Difficulty in Handling Large Search Spaces

In the last few years, intensive research has been done to enhance artificial intelligence AI using optimization techniques. In this paper, we present an extensive review of artificial neural networks ANNs based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm GA, particle swarm optimization PSO, artificial bee colony ABC, and

based on applied cases has been added as the new model training and second method is the genetic algorithm addition to the neural network structure and weights. In this optimization the weight, Bios and the neural networks structure are considered for awareness. The presented method application is assessed in time series prediction flaws. Other

Neural network NN has been tentatively combined into multi-objective genetic algorithms MOGAs to solve the optimization problems in physics. However, the computationally complex physical

NeuralGenetic is a Python project for training neural networks using the genetic algorithm.. NeuralGenetic is part of the PyGAD library which is an open-source Python 3 library for implementing the genetic algorithm and optimizing machine learning algorithms. Both regression and classification neural networks are supported starting from PyGAD 2.7.0. Check documentation of the NeuralGenetic

This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance. By Ahmed Gad , KDnuggets Contributor on March 18, 2019 in AI , Algorithms , Deep Learning , Machine Learning , Neural Networks , numpy , Optimization , Python