Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

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A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

Energies ◽

10.3390/en11102641 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2641 ◽

Cited By ~ 8

Author(s):

Aydin Jadidi ◽

Raimundo Menezes ◽

Nilmar de Souza ◽

Antonio de Castro Lima

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

North Carolina ◽

Feature Selection ◽

Spatial Clustering ◽

Noise Detection ◽

Nsga Ii ◽

Data Set ◽

The Neural Network ◽

Hourly Data

The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.

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A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

Artificial Intelligence Research ◽

10.5430/air.v7n2p43 ◽

2018 ◽

Vol 7 (2) ◽

pp. 43

Author(s):

Abir Alharbi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Feature Selection ◽

Character Recognition ◽

Recognition System ◽

Smart Devices ◽

Binary Matrix ◽

Recognition Systems ◽

Handwritten Recognition ◽

Handwritten Arabic

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

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A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

Lecture Notes in Networks and Systems - Advances in Distributed Computing and Machine Learning ◽

10.1007/978-981-15-4218-3_48 ◽

2020 ◽

pp. 491-500

Author(s):

R. Rathi ◽

D. P. Acharjya

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Feature Selection ◽

Comparative Study ◽

Network Computing ◽

Neural Network Computing

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AN IMPROVED GENETIC ALGORITHM BASED FUZZY-TUNED NEURAL NETWORK

International Journal of Neural Systems ◽

10.1142/s0129065705000438 ◽

2005 ◽

Vol 15 (06) ◽

pp. 457-474 ◽

Cited By ~ 3

Author(s):

S. H. LING ◽

F. H. F. LEUNG ◽

H. K. LAM

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neuron Model ◽

Activation Functions ◽

Improved Genetic Algorithm ◽

Network Function ◽

Fuzzy Network ◽

Neural Fuzzy Network ◽

Neural Fuzzy ◽

Better Than

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

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