Semantically‑oriented mutation operator in cartesian genetic programming for evolutionary circuit design
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Hodan:GPEM,
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author = "David Hodan and Vojtech Mrazek and Zdenek Vasicek",
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title = "Semantically‑oriented mutation operator in cartesian
genetic programming for evolutionary circuit design",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2021",
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volume = "22",
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number = "4",
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pages = "539--572",
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month = dec,
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note = "Special Issue: Highlights of Genetic Programming 2020
Events",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, evolvable hardware, Semantic
operator, Semantic mutation, Evolutionary circuit
design",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/cyKFV",
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DOI = "doi:10.1007/s10710-021-09416-6",
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size = "34 pages",
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abstract = "Cartesian genetic programming (CGP) represents the
most efficient method for the evolution of digital
circuits. Despite many successful applications,
however, CGP suffers from limited scalability,
especially when used for evolutionary circuit design,
i.e. design of circuits from a randomly initialized
population. Considering the multiplier design problem,
for example, the 5 by 5-bit multiplier represents the
most complex circuit designed by the evolution from
scratch. The efficiency of CGP highly depends on the
performance of the point mutation operator, however,
this operator is purely stochastic. This contrasts with
the recent developments in genetic programming (GP),
where advanced informed approaches such as
semantic-aware operators are incorporated to improve
the search space exploration capability of GP. we
propose a semantically-oriented mutation operator
(SOMOk) suitable for the evolutionary design of
combinational circuits. In contrast to standard point
mutation modifying the values of the mutated genes
randomly, the proposed operator uses semantics to
determine the best value for each mutated gene.
Compared to the common CGP and its variants, the
proposed method converges on common Boolean benchmarks
substantially faster while keeping the phenotype size
relatively small. The successfully evolved instances
presented in this paper include 10-bit parity, 10 +
10-bit adder and 5 by 5-bit multiplier. The most
complex circuits were evolved in less than one hour
with a single-thread implementation running on a common
CPU.",
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notes = "Faculty of Information Technology, IT4Innovations
Centre of Excellence, Brno University of Technology,
Brno, Czech Republic",
- }
Genetic Programming entries for
David Hodan
Vojtech Mrazek
Zdenek Vasicek
Citations