Publication Details

Cartesian Genetic Programming as Local Optimizer of Logic Networks

SEKANINA, L.; PTÁK, O.; VAŠÍČEK, Z. Cartesian Genetic Programming as Local Optimizer of Logic Networks. In 2014 IEEE Congress on Evolutionary Computation. Beijing: IEEE Computational Intelligence Society, 2014. p. 2901-2908. ISBN: 978-1-4799-1488-3.
Czech title
Kartézské genetické programování v lokální optimalizaci logických sítí
Type
conference paper
Language
English
Authors
Keywords

logic network, cartesian genetic programming, optimization, digital circuit

Abstract

Logic synthesis and optimization methods work either globally on the whole logic network or locally on preselected subnetworks. Evolutionary design methods have already been applied to evolve and optimize logic circuits at the global level. In this paper, we propose a new method based on Cartesian genetic programming (CGP) as a local area optimizer in combinational logic networks. First, a subcircuit is extracted from a complex circuit, then the subcircuit is optimized by CGP and finally the optimized subcircuit replaces the original one. The procedure is repeated until a termination criterion is satisfied. We present a performance comparison of local and global evolutionary optimization methods with a conventional approach based on ABC and analyze these methods using differently pre-optimized benchmark circuits. If a sufficient time is available, the proposed locally optimizing CGP gives better results than other locally operating methods reported in the literature; however, its performance is significantly worse than the evolutionary global optimization.

Published
2014
Pages
2901–2908
Proceedings
2014 IEEE Congress on Evolutionary Computation
ISBN
978-1-4799-1488-3
Publisher
IEEE Computational Intelligence Society
Place
Beijing
DOI
UT WoS
000356684604023
EID Scopus
BibTeX
@inproceedings{BUT111519,
  author="Lukáš {Sekanina} and Ondřej {Pták} and Zdeněk {Vašíček}",
  title="Cartesian Genetic Programming as Local Optimizer of Logic Networks",
  booktitle="2014 IEEE Congress on Evolutionary Computation",
  year="2014",
  pages="2901--2908",
  publisher="IEEE Computational Intelligence Society",
  address="Beijing",
  doi="10.1109/CEC.2014.6900326",
  isbn="978-1-4799-1488-3",
  url="https://www.fit.vut.cz/research/publication/10504/"
}
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