ISCLEs: Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis

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

@InProceedings{Gao:2008:ICES,

• author = "Peng Gao and Trent McConaghy and Georges Gielen",
• title = "{ISCLEs:} Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis",
• booktitle = "Proceedings of the 8th International Conference on Evolvable Systems, ICES 2008",
• year = "2008",
• editor = "Gregory S. Hornby and Lukas Sekanina and Pauline C. Haddow",
• volume = "5216",
• series = "Lecture Notes in Computer Science",
• pages = "11--21",
• address = "Prague, Czech Republic",
• month = sep # " 21-24",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, EHW",
• isbn13 = "978-3-540-85856-0",
• URL = "http://trent.st/content/2008-ICES-iscles.pdf",
• DOI = "doi:10.1007/978-3-540-85857-7_2",
• size = "11 pages",
• abstract = "Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designer-trustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of weak learners to create an overall circuit ensemble. In ISCLEs, the weak learners are circuit topologies with near-minimal transistor sizes. In each boosting round, first a new weak learner topology and sizings are found via genetic programming-based MOJITO multi-topology optimisation, then it is combined with previous learners into an ensemble, and finally the weak-learning target is updated. Results are shown for the trustworthy synthesis of a sinusoidal function generator, and a 3-bit A/D converter.",
• notes = "Evolvable Systems: From Biology to Hardware",

}

Genetic Programming entries for Peng Gao Trent McConaghy Georges G E Gielen

Citations