In the automotive industry the calibration of the engine control unit is getting more and more complex because of many various boundary conditions, like the demand on
and fuel reduction. One important calibration problem is the parameter estimation for the model of the intake system of the combustion engine. This system is modeled by a physically motivated system, which can be parameterized by black-box models, like neural nets and characteristic diagrams, whose parameters must be set by an intelligent optimizer. Further, two contradictory aims must be considered and the engineer expects at the end of the optimization a pareto-front, where he can choose the best settings for the application from the pareto-optimal parameter estimations. To solve this multi-criteria optimization task an evolutionary algorithm is used, which is a combination of a genetic algorithm and an evolutionary strategy. This evolutionary algorithm is like all other stochastic searching methods leaned on the naturally biological evolution. It combines the well-known covariance matrix adaption for the mutation of the individuals with the S-metric selection for the multi-criteria fitness assignment of the individuals. It also improves these combination by the use of many subpopulations, which work parallel on various clusters, and by the use of an intelligent
-strategy for the initialization of the start individuals. With these improvements the developed evolutionary algorithm can easily fit the model of the intake system to test bed measurements and can provide the user a pareto-optimal set of parameters, on which he can choose on his own that ones, which he find most plausible.