Evolutionary Approximation of Complex Digital Circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@InProceedings{Vasicek:2015:GECCOcomp,

• author = "Zdenek Vasicek and Lukas Sekanina",
• title = "Evolutionary Approximation of Complex Digital Circuits",
• booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• 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",
• isbn13 = "978-1-4503-3488-4",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming: Poster",
• pages = "1505--1506",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739482.2764657",
• DOI = "doi:10.1145/2739482.2764657",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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.",
• notes = "Also known as \cite{2764657} Distributed at GECCO-2015.",

}

Genetic Programming entries for Zdenek Vasicek Lukas Sekanina

Citations