Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{tetteh:2023:Algorithms,
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author = "Michael Tetteh and Allan {de Lima} and
Jack McEllin and Aidan Murphy and Douglas Mota Dias and Conor Ryan",
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title = "Evolving {Multi-Output} Digital Circuits Using
{Multi-Genome} Grammatical Evolution",
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journal = "Algorithms",
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year = "2023",
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volume = "16",
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number = "8",
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pages = "Article No. 365",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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ISSN = "1999-4893",
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URL = "https://www.mdpi.com/1999-4893/16/8/365",
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DOI = "doi:10.3390/a16080365",
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abstract = "Grammatical Evolution is a Genetic Programming variant
which evolves problems in any arbitrary language that
is BNF compliant. Since its inception, Grammatical
Evolution has been used to solve real-world problems in
different domains such as bio-informatics, architecture
design, financial modelling, music, software testing,
game artificial intelligence and parallel programming.
Multi-output problems deal with predicting numerous
output variables simultaneously, a notoriously
difficult problem. We present a Multi-Genome
Grammatical Evolution better suited for tackling
multi-output problems, specifically digital circuits.
The Multi-Genome consists of multiple genomes, each
evolving a solution to a single unique output variable.
Each genome is mapped to create its executable object.
The mapping mechanism, genetic, selection, and
replacement operators have been adapted to make them
well-suited for the Multi-Genome representation and the
implementation of a new wrapping operator.
Additionally, custom grammar syntax rules and a cyclic
dependency-checking algorithm have been presented to
facilitate the evolution of inter-output dependencies
which may exist in multi-output problems. Multi-Genome
Grammatical Evolution is tested on combinational
digital circuit benchmark problems. Results show
Multi-Genome Grammatical Evolution performs
significantly better than standard Grammatical
Evolution on these benchmark problems.",
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notes = "also known as \cite{a16080365}",
- }
Genetic Programming entries for
Michael Tetteh
Allan Danilo de Lima
Jack McEllin
Aidan Murphy
Douglas Mota Dias
Conor Ryan
Citations