Modern technical tasks often need the use of complex system models. In many complex cases the model parameters can be gained using neural networks, but these systems allow only a one-way simulation from the input values to the learned output values. If evaluation in the other direction is needed, these model allow no direct evaluation. This task can be solved using evolutionary algorithms, which are part of the computational intelligence. The term computational intelligence covers three special fields of the artificial intelligence, fuzzy logic, artificial neural networks and evolutionary algorithms. We will focus only on the topic of evolutionary algorithms and fuzzy logic. Evolutionary algorithms covers the fields of genetic algorithms, evolution strategies and evolutionary programming. These methods can be used to optimize technical problems. Evolutionary algorithms have certain advantages, if these problems have no mathematical properties, like steadiness or the possibility to obtain the derivatives. Fuzzy logic systems normally lack these properties. The use of a combination of evolutionary algorithms and fuzzy logic allow an evaluation of the learned simulation models in the direction form output to the input values. An example can be given from the field of screw rotor design.
"Complex system analysis using CI methods", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342888; https://doi.org/10.1117/12.342888