Describing real-world phenomena with complicated models and simulations also gave birth to the need for development of optimization software that would be able to optimize the parameters of such models. To optimize such complicated models, evolutionary algorithms have shown to provide the best performance, in terms of exploration of the search space. However, evolutionary algorithms, in order to solve an optimization problem, generally require a high number of calculations. This weakness is of

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... uch importance that prevents them from being used widely as industrial optimization tools. Meanwhile, the development of another scientific field, that of artificial intelligence, provides optimization researchers with new tools to reduce the computational cost of evolutionary algorithms. An important tool builds on the concept of artificial neural networks, which can be utilized in various different ways to accelerate the convergence of evolutionary algorithms.