Open-ended robust design of analog filters using genetic programming
Jianjun Hu, Xiwei Zhong, Erik D. Goodman · 2005
Most existing research on robust design using evolutionary algorithms (EA) follows the paradigm of traditional robust design, in which parameters of a design solution are tuned to improve the robustness of the system. However, the topological structure of a system may set a limit on the possible robustness achievable through parameter tuning. This paper proposes a new robust design paradigm that exploits the open-ended topological synthesis capability of genetic programming to evolve more robust systems. As a case study, a methodology for automated synthesis of dynamic systems, based on genetic programming and bond graph modeling (GPBG), is applied to evolve robust low-pass and high-pass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), it is shown that open-ended topology search by genetic programming with a fitness criterion rewarding robustness can evolve more robust systems with respect to parameter perturbations than what was achieved through parameter tuning alone, for our test problems.