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The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.