Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming

Dense and Switched Modular Primitives for Bond Graph Model Design

“…There are four types of functions: add functions that can be applied only to a junction and which add a C, I, or R element; insert functions that can be applied to a bond and which insert a 0-junction or 1-junction into the bond; replace functions that can be applied to a node and which can change the type of element and corresponding parameter values for C, I, or R elements; and arithmetic functions that perform arithmetic operations and can be used to determine the numerical values associated with components ( Table 1). Details of function definitions are illustrated in Seo et al [5]. Many redundant or unnecessary junctions and elements were observed in experiments with this basic set.…”

“…In order to overcome this limitation and enable open-ended search, the Bond Graph / Genetic Programming (BG/GP) design methodology has been developed, based on the combination of these two powerful tools (Seo et al [4,5] and tested for a few applications -an analog filter (Fan et al [6]), printer drive mechanism (Fan et. al., [7]), and air pump design (Goodman et al [8]).…”

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

“…It thus does not rely on multiple re-sampling to evaluate the expected fitness, as was used by Wiesmann et al [25]. In Wiesmann et al's approach, each individual is simulated t times to estimate its expected loss function (fitness) defined as 2 , where δ i , i = 1, . .…”

“…Topologically 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.…”

“…Specifically, we proposed a GP-based algorithm to facilitate the design of more robust systems with respect to parameter variations or uncertainty of the design variables. A set of systematic experiments are conducted to verify the following hypotheses about robust design using genetic programming: (1) that dynamic systems with high functional performance evolved by GP without considering a robustness criterion during the evolutionary process may have unacceptably low robustness with respect to parameter perturbation, (2) that the robustness of a system is strongly constrained by its topological/functional structure, and that the amount of robustness improvement available through parameter tuning is limited, and (3) that topologically open-ended synthesis by GP allows for the evolution of more robust solutions than traditional robust design approaches with parameter tuning.…”