Efficient model fitting using a genetic algorithm: pole-zero approximations of HRTFs

We demonstrate that a genetic algorithm (GA) can efficiently generate accurate low-order pole-zero approximations of head-related transfer functions (HRTFs) from measured impulse responses by minimizing a logarithmic error criterion. This approach is much simpler and comparable or superior in efficiency to competing search algorithms. We build on previous work in low-order HRTF approximation. By applying the GA, we converge to solutions of equal quality in about 30 s compared to over 20 min. This work develops a basic steady-state GA using a pole-zero filter design problem as an illustrative example. We propose a domain-appropriate error measure. We then apply the algorithm to designing filters to approximate measured HRTFs. Detailed performance measurements are presented. In the appendix, we propose a widely applicable population variation metric. A lower bound is developed for this metric and is used to detect convergence.