Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InCollection{Aggarwal:2006:GPTP,

• author = "Varun Aggarwal and Una-May O'Reilly",
• title = "Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms",
• 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 = "219--236",
• address = "Ann Arbor",
• month = "11-13 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, circuit sizing, symbolic regression, posynomial models, geometric programming",
• ISBN = "0-387-33375-4",
• URL = "http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf",
• DOI = "doi:10.1007/978-0-387-49650-4_14",
• size = "19 pages",
• abstract = "Starting from a broad description of analogue circuit design in terms of topology design and sizing, we discuss the difficulties of sizing and describe approaches that are manual or automatic. These approaches make use of blackbox optimisation techniques such as evolutionary algorithms or convex optimization techniques such as geometric programming. Geometric programming requires posynomial expressions for a circuit's performance measurements. We show how a genetic algorithm can be exploited to evolve a polynomial expression (i.e. model) of transistor (i.e. mosfet) behaviour more accurately than statistical techniques in the literature.",
• notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop",

}

Genetic Programming entries for Varun Aggarwal Una-May O'Reilly

Citations