In herein presented work, the relation between number of ES iterations and convergence of the whole GPA-ES hybrid algorithm will be studied due to increasing needs to analyze and model large data sets. Evolutionary algorithms are applicable in the areas which are not covered by other artificial intelligence or soft computing techniques like neural networks and deep learning like search of algebraic model of data. | Dependency of GPA-ES algorithm efficiency on ES parameters optimization strength
