How to evolve complex combinational circuits from scratch?
Created by W.Langdon from
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- @InProceedings{Vasicek:2014:ICESa,
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author = "Zdenek Vasicek and Lukas Sekanina",
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title = "How to evolve complex combinational circuits from
scratch?",
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booktitle = "2014 IEEE International Conference on Evolvable
Systems",
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year = "2014",
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pages = "133--140",
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address = "Orlando, FL, USA",
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month = "9-12 " # dec,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4799-4479-8",
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DOI = "doi:10.1109/ICES.2014.7008732",
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size = "8 pages",
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abstract = "One of the serious criticisms of the evolutionary
circuit design method is that it is not suitable for
the design of complex large circuits. This problem is
especially visible in the evolutionary design of
combinational circuits, such as arithmetic circuits, in
which a perfect response is requested for every
possible combination of inputs. This paper deals with a
new method which enables us to evolve complex circuits
from a randomly seeded initial population and without
providing any information about the circuit structure
to the evolutionary algorithm. The proposed solution is
based on an advanced approach to the evaluation of
candidate circuits. Every candidate circuit is
transformed to a corresponding binary decision diagram
(BDD) and its functional similarity is determined
against the specification given as another BDD. The
fitness value is the Hamming distance between the
output vectors of functions represented by the two
BDDs. It is shown in the paper that the BDD-based
evaluation procedure can be performed much faster than
evaluating all possible assignments to the inputs. It
also significantly increases the success rate of the
evolutionary design process. The method is evaluated
using selected benchmark circuits from the LGSynth91
set. For example, a correct implementation was evolved
for a 28-input frg1 circuit. The evolved circuit
contains less gates (a 57percent reduction was
obtained) than the result of a conventional
optimization conducted by ABC.",
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notes = "Also known as \cite{7008732}",
- }
Genetic Programming entries for
Zdenek Vasicek
Lukas Sekanina
Citations