Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Peng:2006:GPTP,

• author = "Xiangdong Peng and Erik D. Goodman and Ronald C. Rosenberg",
• title = "Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs",
• booktitle = "Genetic Programming Theory and Practice {IV}",
• year = "2006",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• volume = "5",
• series = "Genetic and Evolutionary Computation",
• pages = "203--217",
• address = "Ann Arbor",
• month = "11-13 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, bond graph, robust design strategy, Bessel analog filter design",
• ISBN = "0-387-33375-4",
• DOI = "doi:10.1007/978-0-387-49650-4_13",
• size = "17 pages",
• 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.",
• notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop.",

}

Genetic Programming entries for Xiangdong Peng Erik Goodman Ronald C Rosenberg

Citations