In this contribution, a generic applied evolutionary hybrid technique that combines the effectiveness of adaptive multimodel partitioning filters and genetic algorithm (GAs) robustness has been designed, developed, and applied in real-world adaptive system modeling and information mining problems. The method can be applied to linear and nonlinear real-world data, is not restricted to the Gaussian case, is computationally efficient, and is applicable to online/adaptive operation. Furthermore, it can be realized in a parallel processing fashion, a fact that makes it amenable to very large scale integration (VLSI) implementation.