A Novel Evolutionary Algorithm for Designing Robust Analog Filters

Shaobo Li,Jianjun Hu,Wang Zou

doi:10.3390/a11030026

Abstract

Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.

Highlights

Open-ended computational design by genetic programming (GP) has been used for engineering design innovation, with many success stories in a variety of domains including analog circuits, digital circuits, molecular design, and mechatronic systems [1,2]
Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GP-based robust design (GPRD) algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone
For the lowpass filter problem, we found that there is no significant difference in Type-I robustness between GPGARMS and GP

Summary

Introduction

Open-ended computational design by genetic programming (GP) has been used for engineering design innovation, with many success stories in a variety of domains including analog circuits, digital circuits, molecular design, and mechatronic systems [1,2]. This approach uses genetic programming as an open-ended search method for functional design innovation—achieving a specified behavior without pre-specifying the design topology—and has achieved considerable success. Robustness, as the ability of a system to maintain function even with changes in internal structure (including variations in parameters from nominal values) or external environment [3], is critical to engineering design decisions. The other kind is the system robustness with respect to topological perturbation—for example, accidental removal or failure of Algorithms 2018, 11, 26; doi:10.3390/a11030026 www.mdpi.com/journal/algorithms

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Conclusion