Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7764

@Article{tetteh:2023:Algorithms,

• author = "Michael Tetteh and Allan {de Lima} and Jack McEllin and Aidan Murphy and Douglas Mota Dias and Conor Ryan",
• title = "Evolving {Multi-Output} Digital Circuits Using {Multi-Genome} Grammatical Evolution",
• journal = "Algorithms",
• year = "2023",
• volume = "16",
• number = "8",
• pages = "Article No. 365",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• ISSN = "1999-4893",
• URL = "https://www.mdpi.com/1999-4893/16/8/365",
• DOI = "doi:10.3390/a16080365",
• 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.",
• 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