Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength

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 arti cial intelligence or soft computing techniques like neural networks and deep learning like search of algebraic model of data. The di erence between time and algorithmic complexity will be also mentioned as well as the problems of multitasking implementation of GPA, where external in uences complicate increasing of GPA e ciency via Pseudo Random Number Generator (PRNG) choice optimization. Hybrid evolutionary algorithms like GPA-ES uses GPA for solution structure development and Evolutionary Strategy (ES) for parameters identi cation are controlled by many parameters. The most signi cant are sizes of GPA population and sizes of ES populations related to each particular individual in GPA population. There is also limit of ES algorithm evolutionary cycles. This limit plays two contradictory roles. On one side bigger number of ES iterations means less chance to omit good solution for wrongly identi ed parameters, on the opposite side large number of ES iterations signi cantly increases computational time and thus limits application domain of GPA-ES algorithm.