Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction

Abstract

A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

“…Recently, one of the most important fields of interest has been the hybrid approach with SI and machine learning. The number of publications in this domain increased drastically in recent years; some of the most prominent works include hyperparameter optimization [3,18,19], feature selection problems [2], time series prediction tasks, e.g., estimation of COVID-19 cases [6,20], and neural network training [21,22].…”

“…Initialize main metaheuristics control parameters N and T Initialize search space parameters D, u j and l j Initialize CFAEE control parameters γ, β 0 , α 0 , α min , K and φ Generate initial random population P init = {x i,j }, i = 1, 2, 3..., N; j = 1, 2, , ...D using Equation (15) in the search space while t < T do for i = 1 to N do for z = 1 to i do if I z < I i then Move solution z in the direction of individual i in D dimensions (Equation ( 12)) Attractiveness changes with distance r as exp[−γr] (Equation ( 10)) Evaluate new solution, replace the worse individual with better one and update intensity of light (fitness) end if end for end for if t < φ then Replace all solutions for which trial = limit with random ones using Equation ( 15) else Replace all solutions for which trial = limit with guided replacement using Equation ( 16) for k = 1 to K do Perform gBest CLS around the x * using Equations ( 17)-( 19) and generate x * Retain better solution between x * and x * end for end if Update α and λ according to Equations ( 14) and (20), respectively end while Return the best individual x * from the population Post-process results and perform visualization…”

Nature-Inspired Approaches in Software Testing Optimization

“…The most famous representative of this group of algorithms is the genetic algorithm (GA) [4]. It was successfully applied in wide range of the practical NP-hard challenges, such as the load-balancing problem in the cloud-based systems [5], machine learning based Covid-19 prediction [6], design of the convolutional neural networks [7] and many others.…”

“…In the domain of machine learning, SI was utilized to design the convolutional neural networks [23], feed-forward neural network training [24] etc. Nature inspired metaheuristics also show very promising results in time-series prediction, the feature that was used to predict the Covid-19 cases [6] [25]. Finally, the SI approach was used to design a convolutional neural network that performs the classification task of the MRI images of the glioma tumor [26].…”

Improved Harris Hawks Optimization Adapted for Artificial Neural Network Training

“…Also, it is shown that swarm intelligence can be adopted as wrapper methods for feature selection [8,14]. Finally, there are some state-of-the-art approaches that show applications of swarm intelligence for ANNs training [1,6,9,18].…”