Analysis of Simulation-Driven Numerical Performance Modeling Techniques for Application to Analog Circuit Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{McConaghy_2005_iscas_2,

• author = "Trent McConaghy and Georges Gielen",
• title = "Analysis of Simulation-Driven Numerical Performance Modeling Techniques for Application to Analog Circuit Optimization",
• booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)",
• year = "2005",
• volume = "2",
• pages = "1298--1301",
• month = "23-26 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, analog",
• DOI = "doi:10.1109/ISCAS.2005.1464833",
• URL = "http://trent.st/content/2005-ISCAS-blackbox.pdf",
• size = "4 pages",
• abstract = "There is promise of efficiency gains in simulator-in-the-loop analog circuit optimization if one uses numerical performance modeling on simulation data to relate design parameters to performance values. However, the choice of modeling approach can impact performance. We analyze and compare these approaches: polynomials, posynomials, genetic programming, feedforward neural networks, boosted feedforward neural networks, multivariate adaptive regression splines, support vector machines, and kriging. Experiments are conducted on a dataset used previously for posynomial modeling, showing the strengths and weaknesses of the different methods in the context of circuit optimization.",

}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

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