This paper develops an effective algorithm for simultaneous robust adaptive control (filtering) and unknown parameter estimation. The need for adaptive control of poorly known systems is recognized in a diverse range of applications from active noise cancellation and structural vibration isolation to temperature control in process industry, to equalization in communication systems. Due to the nonlinear dependency of the problem on the unknown parameters, an exact solution to this problem is not known to date. The approach discussed here uses a robust adaptive filtering/control algorithm to fulfill the control objectives while a structured search algorithm (Evolutionary Algorithm) identifies the unknown (possibly changing) parameters. This approach overcomes unnecessary approximations that are commonly adopted in the existing solutions by casting the nonlinear parameter estimation problem as an optimization problem. Furthermore, the evolutionary algorithm formulation provides the framework to include the robustness of parameter estimates as an explicit objective in the optimization problem. Robust parameter estimates are of added significance when they are used in a feed-forward control path. Simulation results are presented to demonstrate the feasibility, performance, and the main features of the proposed approach.