Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs.

Genetic programming has been used by Koza and many others to design electrical, mechanical, and mechatronic systems, including systems with both active and passive components. This work has often required large population sizes (on the order of ten thousand) and millions of design evaluations to allow evolution of both the topology and parameters of interesting systems. For several years, the authors have studied the evolution of multi-domain engineering systems represented as bond graphs, a form that provides a unified representation of mechanical, electrical, hydraulic, pneumatic, thermal, and other systems in a uni-fied representation. Using this approach, called the Genetic Programming/Bond Graph (GPBG) approach, they have tried to evolve systems with perhaps tens of components, but looking at only 100,000 or fewer design candidates. The GPBG system uses much smaller population sizes, but seeks to maintain diverse search by using "sustained" evolutionary search processes such as the Hierarchical Fair Competition principle and its derivatives. It uses stochastic setting of parameter values (resistances, capacitances, etc.) as a means of evolving more robust designs. However, in past work, the GPBG system was able to model and simulate only passive components and simple (voltage or current, in the case of electrical systems) sources, which severely restricted the domain of problems it could address. Thus, this paper reports the first steps in enhancing the system to include active components. To date, only three models of a transistor and one model of an operational amplifier (op amp) are analyzed and implemented as two-port bond graph components. The analysis method and design strategy can be easily extended to other models or other active components or even multi-port components. This chapter describes design of an active analog low-pass filter with fifth-order Bessel characteristics. A passive filter with the same characteristics is also evolved with GPBG. Then the best designs emerging from each of these two procedures are compared. [The runs reported here are intended only to document that the analysis tools are working, and to begin study of the effects of stochasticity, but not to determine the power of the design procedure. The initial runs did not use HFC or structure fitness sharing, which will be included as soon as possible. Suitable problems will be tackled, and results with suitable numbers of replicates to allow drawing of statistically valid conclusions will be reported in this paper, to determine whether interesting circuits can be evolved more efficiently in this framework than using other GP approaches.] [ABSTRACT FROM AUTHOR]