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xegaX: A Family of R-Packages for Genetic and Evolutionary Algorithms with Multiple Genome Representations

Geyer-Schulz, Andreas ORCID iD icon 1
1 Institute for Customer Insights (CIN), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

xegaX is a family of R-packages for genetic and evolutionary algorithms with multiple gene representations. At the moment, the following gene representations are supported:
Binary genes, integer genes, real genes, and derivation tree genes.
The package provides a common framework for genetic algorithms with binary genes (sga),
genetic differential evolution algorithms (sgde), genetic algorithms with integer permutations (sgPerm), grammar-based genetic programming algorithms (sgp),
and grammatical evolution algorithms (sge).
The packages have a layered architecture with 4 layers:
The (top-level) main program layer, the population layer which is independent of the gene representation, the gene layer which is split into gene representation dependent (initialization, crossover, mutation, and decoding) and gene representation inpendent
(selection, evaluation) components. In addition, several innovations have been integrated into the package with the aim to improve several architectural goals simultaneously:
Increased flexibility, configurability, and extensibility combined with performance improvements and scalability.
For example, extensive support for parallel and distributed processing has been added: Multi-core processing on notebooks (Linux only), distributed processing on clusters of servers on a local area network (for security reasons), parallel processing on high performance processor clusters based on rmpi.
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Verlagsausgabe §
DOI: 10.5445/IR/1000187255
Veröffentlicht am 24.11.2025
Cover der Publikation
Zugehörige Institution(en) am KIT Institute for Customer Insights (CIN)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 21.11.2025
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000187255
Erschienen in Archives of Data Science, Series A (Online First)
Band 10
Heft 1
Seiten 1-37
Vorab online veröffentlicht am 20.11.2025
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