%0 Book Section %1 Peng:2006:GPTP %A Peng, Xiangdong %A Goodman, Erik D. %A Rosenberg, Ronald C. %B Genetic Programming Theory and Practice IV %C Ann Arbor %D 2006 %E Riolo, Rick L. %E Soule, Terence %E Worzel, Bill %I Springer %K Bessel algorithms, analog bond design filter genetic graph, programming, robust strategy, %P - %T Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs %V 5 %X A possible goal in robust design of dynamic systems is to find a system topology under which the sensitivity of performance to the values of component parameters is minimised. This can provide robust performance in the face of environmental change (resistance variation with temperature, for example) and/or manufacturing-induced variability in parameter values. In some cases, a topology that is relatively insensitive to parameter variation may allow use of less expensive (looser tolerance) components. Cost of components, in some instances, also depends on whether 'standard-sized' components may be used or custom values are required. This is true whether the components are electrical components, mechanical fasteners, or hydraulic fittings. However, using only standardsized or preferred-value components introduces an additional design constraint. This chapter uses genetic programming to develop bond graphs specifying component topology and parameter values for an example task, designing a passive analog low pass filter with fifth-order Bessel characteristics. It explores three alternative design approaches. The first uses 'standard' GP and evolves designs in which components can take on arbitrary values (i.e., custom design). The second approach adds random noise to each parameter and evaluates each design ten times; then, at the end of the evolution, for the best design found, it 'snaps' its parameter values to a small (component specific) set of standard values. The third approach uses only the small set of allowable standard values throughout the evolutionary process, evaluating each design ten times after addition of noise to each standard parameter value. Then the best designs emerging from each of these three procedures are compared for robustness to parameter variation, evaluating each of them one hundred times with random perturbations of their parameters. Results indicated that, for this preliminary study, the third method produced the most robust designs, and the second method was better than the first. %& 5 %@ 0-387-33375-4

@incollection{Peng:2006:GPTP, abstract = {A possible goal in robust design of dynamic systems is to find a system topology under which the sensitivity of performance to the values of component parameters is minimised. This can provide robust performance in the face of environmental change (resistance variation with temperature, for example) and/or manufacturing-induced variability in parameter values. In some cases, a topology that is relatively insensitive to parameter variation may allow use of less expensive (looser tolerance) components. Cost of components, in some instances, also depends on whether 'standard-sized' components may be used or custom values are required. This is true whether the components are electrical components, mechanical fasteners, or hydraulic fittings. However, using only standardsized or preferred-value components introduces an additional design constraint. This chapter uses genetic programming to develop bond graphs specifying component topology and parameter values for an example task, designing a passive analog low pass filter with fifth-order Bessel characteristics. It explores three alternative design approaches. The first uses 'standard' GP and evolves designs in which components can take on arbitrary values (i.e., custom design). The second approach adds random noise to each parameter and evaluates each design ten times; then, at the end of the evolution, for the best design found, it 'snaps' its parameter values to a small (component specific) set of standard values. The third approach uses only the small set of allowable standard values throughout the evolutionary process, evaluating each design ten times after addition of noise to each standard parameter value. Then the best designs emerging from each of these three procedures are compared for robustness to parameter variation, evaluating each of them one hundred times with random perturbations of their parameters. Results indicated that, for this preliminary study, the third method produced the most robust designs, and the second method was better than the first.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Ann Arbor}, author = {Peng, Xiangdong and Goodman, Erik D. and Rosenberg, Ronald C.}, biburl = {https://www.bibsonomy.org/bibtex/249cdaa46d38a71859d119c3928e74c18/brazovayeye}, booktitle = {Genetic Programming Theory and Practice {IV}}, chapter = 5, editor = {Riolo, Rick L. and Soule, Terence and Worzel, Bill}, interhash = {80c0023eebbfea170a9061fdf8d69b84}, intrahash = {49cdaa46d38a71859d119c3928e74c18}, isbn = {0-387-33375-4}, keywords = {Bessel algorithms, analog bond design filter genetic graph, programming, robust strategy,}, month = {11-13 May}, notes = {part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop.}, pages = {-}, publisher = {Springer}, series = {Genetic and Evolutionary Computation}, size = {17 pages}, timestamp = {2008-06-19T17:49:24.000+0200}, title = {Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs}, volume = 5, year = 2006 }