Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming

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@Article{McConaghy:2009:ieeeCADICS,

• author = "Trent McConaghy and Georges G. E. Gielen",
• title = "Template-Free Symbolic Performance Modeling of Analog Circuits via Canonical-Form Functions and Genetic Programming",
• journal = "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems",
• year = "2009",
• volume = "28",
• pages = "1162--1175",
• number = "8",
• month = aug,
• keywords = "genetic algorithms, genetic programming, SPICE, analogue circuits CAFFEINE, SPICE simulation data, analog circuits, arbitrary nonlinear circuits, canonical-form functions, compact interpretable symbolic performance models, kriging, neural networks, posynomials, product-of-sum layers, splines, sum-of-product layers, support vector machines, template-free symbolic performance modeling",
• ISSN = "0278-0070",
• URL = "http://trent.st/content/2009-TCAD-caffeine_scale.pdf",
• DOI = "doi:10.1109/TCAD.2009.2021034",
• size = "14 pages",
• abstract = "This paper presents CAFFEINE, a method to automatically generate compact interpretable symbolic performance models of analog circuits with no prior specification of an equation template. CAFFEINE uses SPICE simulation data to model arbitrary nonlinear circuits and circuit characteristics. CAFFEINE expressions are canonical-form functions: product-of-sum layers alternating with sum-of-product layers, as defined by a grammar. Multiobjective genetic programming trades off error with model complexity. On test problems, CAFFEINE models demonstrate lower prediction error than posynomials, splines, neural networks, kriging, and support vector machines. This paper also demonstrates techniques to scale CAFFEINE to larger problems.",
• notes = "Also known as \cite{5166638}",

}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

Citations