  Solving an optimization task in any domain is a very challenging problem,
especially when dealing with nonlinear problems and non-convex functions. Many
meta-heuristic algorithms are very efficient when solving nonlinear functions.
A meta-heuristic algorithm is a problem-independent technique that can be
applied to a broad range of problems. In this experiment, some of the
evolutionary algorithms will be tested, evaluated, and compared with each
other. We will go through the Genetic Algorithm\, Differential Evolution,
Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated
Annealing. They will be evaluated against the performance from many points of
view like how the algorithm performs throughout generations and how the
algorithm's result is close to the optimal result. Other points of evaluation
are discussed in depth in later sections.
