Algorithms based on the evolutionary mechanisms of genetic mutation and crossover have the potential to yield substantial advantages compared to conventional design. In the specific case of filter design, evolutionary convergence potentially allows a solution that requires less energy and hardware to implement. However, the potential benefits of exploiting evolution in hardware are still being researched. To explore the characteristics of such algorithms in hardware, an evolutionary digital filtering algorithm was created and optimized on MATLAB. The filter design algorithm eventually achieved performance comparable to conventional Finite Impulse Response (FIR) filters, but suffered from limited design speed due to a large amount of software overhead. The advantage was that the evolved filter lent itself to a higher degree of customizable parameters, as the performance evaluation module was relatively simplistic compared to those of a Least-Squares or Parks-McClellan FIR filter. After evaluating the performance of the algorithm against conventional filtering algorithms, the subset of the algorithm was written in a Hardware Description Language (HDL) and simulated to determine the constraints on a hardware platform such as a Field Programmable Gate Array (FPGA). The results from these studies show that the evolutionary algorithm will reap advantages in unique filter yield and code simplicity for similar performance to conventional filter designs. In essence, the evolved filter platform lends itself to a greater degree of customizability, as the parameters for performance evaluation can easily be changed without modifying the rest of the algorithm.