The results of iterative approaches to designing high-performance filters can depend on the choice of initial parameters, which may produce local optima but miss better solutions farther off in the design space. A homotropic approach, applied here for the first time, can improve optimizations, using an artificial neural network to replace time-consuming electromagnetic modeling. Design, simulation, and testing of an all-pole filter and a generalized Chebyshev filter validate the method.