Maximizing a Nonlinear Function using Evolutionary Strategies and Parameter Tuning

Abstract

Maximization and minimization of functions are problems that occur in most scientific fields. There are several methods for solving them and one such method is the evo- lutionary strategy algorithm, which is an algorithm inspired by the biological theory of evolution. The algorithm works with a set of candidate solutions, and in each iteration, these solutions are crossed and mutated to create new ones. The newly created solutions are then evaluated and the best solutions are moved to the next iteration. This process is repeated until the optimal solution is found or another terminating condition occurs. In this paper, we implemented such an algorithm and used it to maximize the chosen function. We also found the best combination of parameters for which the algorithm gives the best result.