Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters

The design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. The constraints are high and a large number of parameters have to be estimated, especially when dealing with 2-D FIR filters. In order to improve the performance of conventional approaches, we explore several stochastic methodologies capable of handling large spaces. We specifically propose a new genetic algorithm in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The algorithm is globally improved by adapting the mutation and crossover and selection operators with the genetic advances. A dynamic ranking selection scheme is introduced to limit the promotion of extraordinary chromosomes. A refreshing mechanism is investigated to manage the tradeoff between diversity and elitism. The key point of our approach stems from the capacity of the genetic algorithm (GA) to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Most of parameters and operators are changed by the GA itself. From an initial calibration, the GA performs the design problem while calibrating and repeatedly re-calibrating itself for solving it. We demonstrate on various cases of filter design a significant improvement in performance.