COVID-19 Outbreak Prediction with Machine Learning

Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson

Genetic Algorithm (GA) GAs are considered a subset of "computational models" inspired by the concept of evolution [49] . These algorithms use "Potential Solutions" or "Candidate Solutions" or "Possible Hypotheses" for a specific problem in a "chromosome-like" data structure. GA maintains vital information stored in these chromosome data structures by applying "Recombination Operators" to chromosome-like data structures [50] [51] [52] [53] . In many cases, GAs are employed as "Function Optimizer" algorithms, which are algorithms used to optimize "Objective Functions." Of course, the range of applications that use the GA to solve problems is very wide [52, 54] . The implementation of the GA usually begins with the production of a population of chromosomes generated randomly and bound up and down by the variables of the problem. In the next step, the generated data structures (chromosomes) are evaluated, and chromosomes that can better display the optimal solution of the problem are more likely to be used to produce new chromosomes. The degree of "goodness" of an answer is usually measured by the population of the current candidate's answers [55] [56] [57] [58] [59] . The main algorithm of a GA process is demonstrated in Figure 3 . In the present study, GA [59] was employed for estimation of the parameters of Eq. 6 to 13. The population number was selected to be 300 and the maximum generation (as iteration number) was determined to be 500 according to different trial and error processes to reduce the cost function value. The cost function was defined as the mean square error between the target and estimated values according to Eq. 14: