Evolutionary Approximation of Complex Digital Circuits
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Vasicek:2015:GECCOcomp,
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author = "Zdenek Vasicek and Lukas Sekanina",
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title = "Evolutionary Approximation of Complex Digital
Circuits",
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booktitle = "GECCO Companion '15: Proceedings of the Companion
Publication of the 2015 Annual Conference on Genetic
and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3488-4",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming: Poster",
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pages = "1505--1506",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739482.2764657",
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DOI = "doi:10.1145/2739482.2764657",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Circuit approximation has been developed in recent
years as a viable method for constructing energy
efficient electronic systems. An open problem is how to
effectively obtain approximate circuits showing good
compromises between key circuit parameters -- the
error, power consumption, area and delay. The use of
evolutionary algorithms in the task of circuit
approximation has led to promising results; however,
only relative simple circuit instances have been
tackled because of the scalability problems of the
evolutionary design method. We propose to replace the
most time consuming part of the evolutionary design
algorithm, i.e. the fitness calculation exponentially
depending on the number of circuit inputs, by an
equivalence checking algorithm operating over Binary
Decision Diagrams (BDDs). Approximate circuits are
evolved using Cartesian genetic programming which calls
a BDD solver to calculate the fitness value of
candidate circuits. The method enables to obtain
approximate circuits consisting of tens of inputs and
hundreds of gates and showing desired trade-off between
key circuit parameters.",
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notes = "Also known as \cite{2764657} Distributed at
GECCO-2015.",
- }
Genetic Programming entries for
Zdenek Vasicek
Lukas Sekanina
Citations