Most of the machine learning methods, including evolutionary algorithms, base their training on the optimization of the objective function. The main focus underlying the methods of optimization of the objective function is that the best way to improve the performance of a solver is to reward them for getting closer to the goal. In most evolutionary algorithms, the closeness to the goal is measured by the fitness of the solver. The measure of an organism's performance is defined by the fitness function, which is a metaphor for evolutionary pressure on the organism to adapt to its environment. According to that paradigm, the fittest organism is better adapted to its environment and best suited to find a solution.
While direct fitness function optimization methods work well in many simple cases, for more complex tasks, it often falls victim...
